bioRxiv Subject Collection: Neuroscience's Journal

Comparative study of enriched dopaminergic neurons from siblings with Gaucher disease discordant for parkinsonism
Inducible pluripotent stem cells (iPSCs) derived from patient samples have significantly enhanced our ability to model neurological diseases. Comparative studies of dopaminergic (DA) neurons differentiated from iPSCs derived from siblings with Gaucher disease discordant for parkinsonism provides a valuable avenue to explore genetic modifiers contributing to GBA1-associated parkinsonism in disease-relevant cells. However, such studies are often complicated by the inherent heterogeneity in differentiation efficiency among iPSC lines derived from different individuals. To address this technical challenge, we devised a selection strategy to enrich dopaminergic (DA) neurons expressing tyrosine hydroxylase (TH). A neomycin resistance gene (neo) was inserted at the C-terminus of the TH gene following a T2A self-cleavage peptide, placing its expression under the control of the TH promoter. This allows for TH+ DA neuron enrichment through geneticin selection. This method enabled us to generate comparable, high-purity DA neuron cultures from iPSC lines derived from three sisters that we followed for over a decade: one sibling is a healthy individual, and the other two have Gaucher disease (GD) with GBA1 genotype N370S/c.203delC+R257X (p.N409S/c.203delC+p.R296X). Notably, the younger sister with GD later developed Parkinson disease (PD). A comprehensive analysis of these high-purity DA neurons revealed that although GD DA neurons exhibited decreased levels of glucocerebrosidase (GCase), there was no substantial difference in GCase protein levels or lipid substrate accumulation between DA neurons from the GD and GD/PD sisters, suggesting that the PD discordance is related to of other genetic modifiers.

Human stem cell derived neurons and astrocytes to detect auto-reactive IgG in neurological diseases
Up to 46% of patients with presumed autoimmune limbic encephalitis are seronegative for all currently known CNS antigens. We developed a cell-based assay (CBA) to screen for novel neural antibodies in serum and cerebrospinal fluid (CSF) using neurons and astrocytes derived from human induced pluripotent stem cells (hiPSC). In this study, hiPSC-derived neural cells seeded on 96-well plates (96-well CBA) were incubated with serum or CSF from 99 patients, including 42 with inflammatory neurological diseases (IND) and 57 with non-IND (NIND). The IND group included 11 patients with previously established neural antibodies, six with seronegative neuromyelitis optica spectrum disorder (NMOSD), 12 with suspected autoimmune encephalitis/paraneoplastic syndrome (AIE/PNS), and 13 with other IND (OIND). IgG bound to fixed CNS cells were detected using a combination of fluorescently-labelled antibodies. IgG-associated fluorescence intensity measures and microscopy observations were automated. IgG reactivity to neurons and astrocytes was further analyzed by flow cytometry. Peripheral blood mononuclear cells (PBMC) were used as CNS-irrelevant control target cells. Reactivity profile was defined as positive using a Robust regression and Outlier removal test with a false discovery rate at 10% following each individual readout. Using our 96-well CBA, we detected antibodies recognizing hiPSC-derived neural cells in 19/99 (19.2%) study patients. Antibodies bound specifically to astrocytes in nine cases, to neurons in eight cases and to both cell types in two cases. Microscopy single-cell analyses ascertained cellular distribution and binding specificity. Highlighting the significance of our novel 96-well CBA assay, the occurrence of CNS-specific antibody binding was more frequent in IND (15/42) than in NIND patients (4/57) (Fisher test, p=0.0005). Three of three patients with astrocyte-reactive (2 AQP4+ NMO, 1 GFAP astrocytopathy), and 3/4 with intracellular neuron-reactive antibodies (2 Hu+, 1 Ri+ AIE/PNS), as identified in validated diagnostic laboratories, were also positive with our CBA assay. Most interestingly, we showed antibody-reactivity in 2/6 seronegative NMOSD, 6/12 probable AIE/PNS, and 1/13 OIND. Flow cytometry using hiPSC-derived CNS cells or PBMC detected antibody binding in 13 versus 0 patients, respectively, establishing the specificity of the detected antibodies for neural tissue. Our unique hiPSC-based 96-well CBA allows for the screening of neuron- or astrocyte-reactive antibodies in patients with suspected immune-mediated neurological syndromes, and negative testing in established routine laboratories. Such a potent tool opens new perspectives in establishing early diagnosis of Ab-mediated diseases of the CNS.

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression
Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli
The extensive Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes detailed emotion annotations by 44 raters for 14 short films with a combined duration of over 2 1/2 hours, as well as recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a different sample of 30 individuals watching the same films. The detailed annotations of experienced emotion evaluated 50 items including ratings of discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. Quality assessment for the behavioural data shows a mean inter-rater agreement of 0.38. The parallel fMRI data was acquired at 3 Tesla in four sessions, accompanied with a high-resolution structural (T1) and resting state fMRI scans for each participant. Physiological recordings during fMRI included heart rate, respiration, and electrodermal activity (EDA). Quality assessment indicators confirm acceptable quality of the MRI data. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. A particular strength of this data is the high temporal resolution of behavioural annotations, as well as the inclusion of a validation study in the fMRI sample. This high-quality behavioural data in combination with continuous physiological and MRI measurements makes this dataset a treasure trove for researching human emotion in response to naturalistic stimulation in a multimodal framework.

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality
Scaling relationships characterize complex systems at criticality. In the brain, these relationships are evident in scale-invariant activity cascades, so-called neuronal avalanches, quantified by power laws in avalanche size and duration. At the cellular level, neuronal avalanches are identified in spatially distributed groups of neurons that participate in cascades of coincident action potential firing. Such spatiotemporal synchronization is central to theories on brain function, yet scaling relationships in avalanche synchronization have been challenging to study when only a fraction of neurons is observed, underestimating avalanche properties. Here, we study these biases from fractional sampling in an all-to-all, balanced network of excitatory and inhibitory neurons with critical branching process dynamics. We focus on the growth of mean avalanche size with avalanche duration. For parabolic avalanches, this growth is quadratic, quantified by the scaling exponent, {chi} = 2, which signifies rapid spatial expansion of coincident firing within a relatively short period of time. In contrast, {chi} << 2 for fractionally sampled networks. We show that temporal coarse-graining combined with a threshold for the minimally required coincident firing in the network recovers {chi} = 2, even when sampling as few as 0.1% of the neurons. In contrast, a commonly proposed 'crackling noise' approach fails to recover {chi} under those conditions. Our approach robustly identifies {chi} = 2 for ongoing neuronal activity in frontal cortex of awake mice using cellular 2-photon imaging. Our findings demonstrate how to correct scaling bias from fractional sampling and identifies rapid, scale-invariant synchronization of cell assemblies in the brain.

Sample preparation methods for volume electron microscopy in mollusc Berghia stephanieae
Creating a high-resolution brain atlas in diverse species offers crucial insights into general principles underlying brain function and development. A volume electron microscopy approach to generate such neural maps has been gaining importance due to advances in imaging, data storage capabilities, and data analysis protocols. Sample preparation remains challenging and is a crucial step to accelerate the imaging and data processing pipeline. Here, we introduce several replicable methods for processing the brains of the gastropod mollusc, Berghia stephanieae for volume electron microscopy. Although high-pressure freezing is the most reliable method, the depth of cryopreservation is a severe limitation for large tissue samples. We introduce a BROPA-based method using pyrogallol and methods to rapidly process samples that can save hours at the bench. This is the first report on sample preparation and imaging pipeline for volume electron microscopy in a gastropod mollusc, opening up the potential for connectomic analysis and comparisons with other phyla.

Adoptive transfer of mitochondrial antigen-specific CD8+ T-cells in mice causes parkinsonism and compromises the dopamine system
The progressive dysfunction and degeneration of dopamine (DA) neurons of the ventral midbrain is linked to the development of motor symptoms in Parkinson's disease (PD). Multiple lines of evidence suggest the implication of neuroinflammation and mitochondrial dysfunction as key drivers of neurodegenerative mechanisms in PD. Recent work has revealed that loss of the mitochondrial kinase PINK1 leads to enhanced mitochondrial antigen presentation (MitAP) by antigen-presenting cells (APCs), the amplification of mitochondrial antigen-specific CD8+ T cells and the loss of DA neuron terminals markers in the brain in response to gut infection. However, whether mitochondrial antigen-specific T cells are involved in and/or sufficient to cause DA system dysfunction remains unclear. Here, we investigated the effect of mitochondrial autoimmunity by adoptively transferring mitochondrial peptide-specific CD8+ T cells into wild-type (WT) and PINK1 KO mice. We find that this leads to L-DOPA-reversible motor impairment and to robust loss of DA neurons and axonal markers in the striatum in both PINK1 WT and KO mice. Our findings provide direct evidence of the pivotal role played by mitochondrial-specific CD8+ T cell infiltration in the brain in driving PD-like pathology and the development of parkinsonism. Altogether, our data strongly support the hypothesis that MitAP and autoimmune mechanisms play a key role in the pathophysiological processes leading to PD.

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks
The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the causal significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp EEG) recordings in patients during presurgical intracerebral electrical stimulation (iES). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence of recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show a more functionally integrated processing style than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure.

Decoding the spatiotemporal dynamic neural representation of repetitive facial expression imitation
Imitating facial emotion expressions can facilitate social interactions, although the behavioral and neural spatiotemporal dynamics is unclear. Here participants (N=100) imitated facial emotions repeatedly over one month (16 times in total) with neural activity measured on three occasions using functional near-infrared spectroscopy. Additionally, the transfer effect of repeated imitation on emotional face and scene perception was assessed by fMRI with multivariate pattern analysis. Valence specific imitation performance was facilitated by the alterations in the similarity of spatiotemporal patterns evoked in the mirror neuron system (MNS) with information flow moving progressively towards the inferior frontal gyrus as the as the number of times of imitation increase. Furthermore, MNS representation predictive patterns of processing emotional faces, but not scenes, were enhanced. Overall, these findings provide a neural changes of information flow within MNS and advance our understanding of the spatiotemporal dynamics from novice to proficient of facial emotion imitation.

Controlling the human connectome with spatially diffuse input signals
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state--a whole-brain pattern of activity--to another. Network control theory offers a framework for understanding the effort -- energy -- associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex -- geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

AI Derived Therapeutic Development for the Treatment of Opioid Use Disorder
The opioid epidemic has led to a devastating loss of life nationwide. Of those dependent on opioids, many individuals desire to quit or reduce use, but their efforts are often unsuccessful given the powerful reinforcing properties associated with opioid drugs, especially fentanyl given its high potency and speed of onset. Here, we developed a novel theraputic based on a newly developed artificial intelligence (AI)-based platform, which was rationally designed to identify markers of dysregulation from human drug user postmortem brain tissue. The GATC-021 compound was synthesized and validated with in vitro screening for target specificity. Thereafter, GATC-021 was examined for its effectiveness in modulating opioid dependence with an animal model of addiction. We found that GATC-021 substantially reduced fentanyl intake in both male and female rats, as assessed with intravenous self-administration. However, given drug soluability challenges, additional studies are needed to better develop drug formulations to permit translation into clinical populations more effectively. Taken together, these findings validate our AI-based platform for novel therapeutic development with a polypharmacy approach and further support the effectiveness of such target modulation as a promising therapeutic approach for those suffering from opioid use disorder.

The intracellular Ca2+ sensitivity of transmitter release from neocortical boutons
The Ca2+ sensitivity of transmitter release is a major determinant of synaptic reliability and plasticity. Two Synaptotagmin (Syt) isoforms, Syt1 and Syt2, are the main Ca2+ sensors triggering action potential-evoked release in the brain1. However, only the Ca2+-sensitivity of Syt2-triggered release from hindbrain synapses has been studied in detail2-4. For Syt1, the dominating isoform in the forebrain, quantitative detail is lacking. Here, we quantified the Ca2+-dependency of Syt1-triggered release at synapses connecting neocortical layer 5 pyramidal neurons by combining Ca2+-uncaging, two-photon Ca2+-imaging and patch-clamp electrophysiology. The Ca2+-dependency of Syt1-triggered release had high affinity (EC50, 20 M) and positive cooperativity with a Hill coefficient of 3.57. It was steep in a dynamic range between ~10 and ~30 M that was also covered by action potential-evoked release. We establish a kinetic model that quantifies the dependency of Syt1-triggered release on presynaptic Ca2+ concentrations. Compared to Syt2 models2-4, Syt1-triggered release had three to four-fold higher affinity and a steeper Ca2+-dependency in the dynamic range. Different from Syt2, it showed pronounced saturation already at Ca2+ elevations above ~35 M. Thus, Syt1 optimizes synapses for high reliability at moderate local Ca2+ elevations and for high plastic controllability of release within the steep dynamic range.

A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex
Understanding 3D representations of spatial information, particularly in naturalistic scenes, remains a significant challenge in vision science. This is largely because of conceptual difficulties in disentangling higher-level 3D information from co-occurring features and cues (e.g., the 3D shape of a scene image is necessarily defined by spatial frequency and orientation information). Recent work has employed newer models and analysis techniques that attempt to mitigate these in-principle difficulties. For example, one such study reported 3D-surface features were uniquely present in areas OPA, PPA, and MPA/RSC (areas typically referred to as 'scene- selective'), above and beyond a Gabor-wavelet baseline ("2D") model. Here, we tested whether these findings generalized to a new stimulus set that, on average, dissociated static Gabor-wavelet baseline ("2D") features from 3D scene-surface features. Surprisingly, we found evidence that a Gabor-wavelet baseline model better fit voxel responses in areas OPA, PPA and MPA/RSC compared to a model with 3D-surface information. This raises the question of whether previous findings of greater 3D information could have been due to a baseline condition that didn't model some potentially critical low-level features (e.g., motion). Our findings also emphasize that much of the information in scene-responsive regions--potentially even information about 3D surfaces--may be in the form of spatial frequency and orientation information often considered 2D or low-level, and they highlight continued fundamental conceptual challenges in disentangling the contributions of low-level vs. high-level features in visual cortex.

CryoVesNet: A Dedicated Framework for Synaptic Vesicle Segmentation in Cryo Electron Tomograms
Cryo-electron Tomography (Cryo-ET) has the potential to reveal cell structure down to atomic resolution. Nevertheless, cellular cryo-ET data is often highly complex, and visualization, as well as quantification, of subcellular structures require image segmentation. Due to a relatively high level of noise and anisotropic resolution in cryo-ET data, automatic segmentation based on classical computer vision approaches usually does not perform satisfactorily. For this reason, cryo-ET researchers have mostly performed manual segmentation. Communication between neurons relies on neurotransmitter-filled synaptic vesicle (SV) exocytosis. Recruitment of SVs to the plasma membrane is an important means of regulating exocytosis and is influenced by interactions between SVs. Cryo-ET study of the spatial organization of SVs and of their interconnections allows a better understanding of the mechanisms of exocytosis regulation. Extremely accurate SV segmentation is a prerequisite to obtaining a faithful representation of SVs state of connectivity. Hundreds to thousands of SVs are present in a typical synapse, and their time-consuming manual segmentation is a bottleneck in this analysis. Several attempts to automate vesicle segmentation by classical computer vision or machine learning algorithms have not yielded robust results. We addressed this problem by designing a workflow consisting of a U-Net convolutional segmentation network followed by post-processing steps. This combination yields highly accurate results. Furthermore, we provide an interactive tool for accurately segmenting spherical vesicles in a fraction of the time required by available manual segmentation methods. This tool can be used to segment vesicles that were missed by the fully automatic procedure or to quickly segment a handful of vesicles while bypassing the fully automatic procedure. Our pipeline can in principle be used to segment any spherical vesicle in any cell type as well as extracellular vesicles.

Minimally invasive serial collection of cerebrospinal fluid reveals sex-dependent differences in neuroinflammation in a rat model of mild traumatic brain injury
Traumatic brain injuries (TBI) are the seventh leading cause of disability globally with 48.99 million prevalent cases and 7.08 million years lived with diability. Approximately 80% of TBI patients are diagnosed with mild TBI (mTBI), or concussion, caused by nonpenetrating mechanical trauma to the head or body along with sudden rotational motion of the head. Major distinctions between mTBI and more severe TBI include the absence of intracranial lesions, hemorrhages, and skull fractures in mTBI. Further, there are differences in the inflammatory response based on injury severity. While some overlap of the neuroinflammatory response across TBI severities is expected, studies investigating the temporal dynamics of neuroinflammation after mTBI and the sex-dependent differences are still needed as there have been many reported sex-dependent differences in mTBI. To fully understand the inflammatory response to TBI, it is vital to assay cerebrospinal fluid (CSF), as it has been shown that CSF biomarkers such as MAP-2 enhance prognosis capabilities. The literature analyzing the CSF proteome over time after TBI is very limited, primarily due to the lack of a CSF collection method that is minimally invasive and enables serial collections. This study had three primary goals. First, we wanted to describe a method of minimally invasive serial CSF collection. The method described in this study can easily be adapted by any laboratory prepared for animal studies. Second, we wanted to confirm that serial collection of the CSF does not alter CSF protein levels. Of the 17 analytes we tested, 16 showed no difference between serial collection and single collection. Third, we wanted to establish a framework for assessing sex differences in neuroinflammation after a concussion. We showed that results vary based on the framing of the statistical test. However, it is evident that males experience a more robust inflammatory response to a single concussion than females.

A novel interface for cortical columnar neuromodulation with multi-point infrared neural stimulation
Cutting edge advances in electrical visual cortical prosthetics (VCPs) have evoked perception of shapes, motion, and letters in human and nonhuman primates. However, there is no existing method that employs a targeted columnar approach with a non-penetrating array of stimulation points. Neither has there been a direct demonstration that higher order cortical response can be elicited from externally stimulated lower order cortical sites. Here, we take an approach that employs images maps of cortical columns combined with delivery of optical stimulation through a fiber optic array to stimulate selected sets of columns. Specifically, using infrared neural stimulation (INS) delivered through a linear optic fiber array in anesthetized cat visual cortex, we predicted that the orientation of the array would selectively activate different higher order orientation domains in contralateral cat area 18. We found that INS modulation of response to ongoing visual oriented gratings produced enhanced responses in orientation-matched domains and reduced response in non-matched domains, consistent with a known higher order integration mediated by callosal inputs. This establishes proof-of-principle that an external device can access existing cortical circuitry via a column-targeted approach, and provides groundwork for a targeted column-based approach to cortical prosthetics using dense optical fiber bundle arrays.

Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.
In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naive controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI1) and after (MRI2) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naive controls no difference was found between MRI1 and MRI2. Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

Distal tuft dendrites shape and maintain new place fields
Hippocampal pyramidal neurons support episodic memory by integrating complementary information streams into new "place fields". Distal tuft dendrites are thought to initiate place field formation via plateau potentials. However, the hitherto experimental inaccessibility of this dendritic compartment has rendered its in vivo function entirely unknown. We report that distal tuft dendrites are variably recruited during place field formation in mouse area CA1. This variability predicts place field information content and may account for the unique and unexplained association window underpinning place field formation. Surprisingly, tuft-associated plateau potentials primarily occur during subsequent place field traversals and may serve a maintenance function alongside robust local spatial tuning. Our findings represent a significant advance toward a mechanistic, subcellular understanding of memory formation in the hippocampus.

Evoked Resonant Neural Activity Long-Term Dynamics can be Reproduced by a Computational Model with Vesicle Depletion
Subthalamic deep brain stimulation (DBS) robustly generates high-frequency oscillations known as evoked resonant neural activity (ERNA). Recently the importance of ERNA has been demonstrated through its ability to predict the optimal DBS contact in the subthalamic nucleus in patients with Parkinsons disease. However, the underlying mechanisms of ERNA are not well understood, and previous modelling efforts have not managed to reproduce the wealth of published data describing the dynamics of ERNA. Here, we therefore aim to present a minimal model capable of reproducing the characteristics of the slow ERNA dynamics published to date. We make biophysically-motivated modifications to the Kuramoto model and fit its parameters to the slow dynamics of ERNA obtained from data. We further validate the model against experimental data from Parkinson's disease patients by simulating variable stimulation and medication states, as well as the response of individual neurons. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA with a single neuronal population, and, crucially, with vesicle depletion as the key mechanism behind the ERNA frequency decay. We provide a series of predictions from the model that could be the subject of future studies for further validation.

Mechanosensory cephalic bristles mediate rapid flight initiation in endothermic hawkmoths
Endothermic insects including bees, butterflies, and moths need to warm up their flight muscles before taking flight. For instance, diurnal butterflies bask in the sun to heat their flight muscles, whereas nocturnal hawkmoths display a pre-flight shivering behavior in which small-amplitude wing movements cause flight muscles to warm up, eventually generating large-amplitude wing motion for flight. The time required for warm-up puts such insects at a considerable risk if they need to rapidly escape from predators. Here, we show that upon experiencing a sudden air-puff on the head, hawkmoths rapidly initiate flight bypassing the pre-flight shivering phase. This response is mediated by mechanosensory cephalic bristles that are buried under the scales on their head. Cephalic bristle mediated flight entails a stereotypic triggering of various flight-related reflexes including antennal positioning, foreleg extension, wing movement, and abdominal flexion. Some mechanosensory neurons underlying cephalic bristles arborize in the subesophageal zone (SEZ) and antennal motor and mechanonsensory center (AMMC), whereas most arborize in pro-, meso- and meta-thoracic ganglia which contain the motor circuitry for foreleg motion, flight, and abdominal flexion. Thermal recordings revealed that large-amplitude wing motion following cephalic bristle-stimulation occurs at lower thoracic temperatures than required for voluntary flight. Electromyogram recordings from steering and indirect flight muscles show significant variability in activation latency in response to cephalic bristle stimulus. The range of latency values among different muscles overlaps, suggesting that cephalic bristle stimulation activates steering muscles, thereby generating high-amplitude wing movement at lower thoracic temperatures. Concomitant activation of the indirect flight muscles initiates thoracic warm-up in preparation for longer flight. Thus, akin to locusts, the cephalic bristle system in hawkmoths rapidly triggers flight upon sensing danger, ensuring swift escape from potential threats.

NF-kB/NLRP3 Translational Inhibition by Nanoligomer Therapy Mitigates Ethanol and Advanced Age-Related Neuroinflammation
Binge alcohol use is increasing among aged adults (>65 years). Alcohol-related toxicity in aged adults is associated with neurodegeneration, yet the molecular underpinnings of age-related sensitivity to alcohol are not well described. Studies utilizing rodent models of neurodegenerative disease reveal heightened activation of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-{kappa}B) and Nod like receptor 3 (NLRP3) mediate microglia activation and associated neuronal injury. Our group, and others, have implicated hippocampal-resident microglia as key producers of inflammatory mediators, yet the link between inflammation and neurodegeneration has not been established in models of binge ethanol exposure and advanced age. Here, we report binge ethanol increased the proportion of NLRP3+ microglia in the hippocampus of aged (18-20 months) female C57BL/6N mice compared to young (3-4 months). In primary microglia, ethanol-induced expression of reactivity markers and NLRP3 inflammasome activation were more pronounced in microglia from aged mice compared to young. Making use of an NLRP3-specific inhibitor (OLT1177) and a novel brain-penetrant Nanoligomer that inhibits NF-{kappa}B and NLRP3 translation (SB_NI_112), we find ethanol-induced microglial reactivity can be attenuated by OLT1177 and SB_NI_112 in microglia from aged mice. In a model of intermittent binge ethanol exposure, SB_NI_112 prevented ethanol-mediated microglia reactivity, IL-1{beta} production, and tau hyperphosphorylation in the hippocampus of aged mice. These data suggest early indicators of neurodegeneration occurring with advanced age and binge ethanol exposure are NF-kB- and NLRP3-dependent. Further investigation is warranted to explore the use of targeted immunosuppression via Nanoligomers to attenuate neuroinflammation after alcohol consumption in the aged.

Opposing Motor Memories in the Direct and Indirect Pathways of the Basal Ganglia
Loss of dopamine neurons causes motor deterioration in Parkinson's disease patients. We have previously reported that in addition to acute motor impairment, the impaired motor behavior is encoded into long-term memory in an experience-dependent and task-specific manner, a phenomenon we refer to as aberrant inhibitory motor learning. Although normal motor learning and aberrant inhibitory learning oppose each other and this is manifested in apparent motor performance, in the present study, we found that normal motor memory acquired prior to aberrant inhibitory learning remains preserved in the brain, suggesting the existence of independent storage. To investigate the neuronal circuits underlying these two opposing memories, we took advantage of the RNA-binding protein YTHDF1, an m6A RNA methylation reader involved in the regulation of protein synthesis and learning/memory. Conditional deletion of Ythdf1 in either D1 or D2 receptor-expressing neurons revealed that normal motor memory is stored in the D1 (direct) pathway of the basal ganglia, while inhibitory memory is stored in the D2 (indirect) pathway. Furthermore, fiber photometry recordings of GCaMP signals from striatal D1 (dSPN) and D2 (iSPN) receptor-expressing neurons support the preservation of normal memory in the direct pathway after aberrant inhibitory learning, with activities of dSPN predictive of motor performance. Finally, a computational model based on activities of motor cortical neurons, dSPN and iSPN neurons, and their interactions through the basal ganglia loops supports the above observations. These findings have important implications for novel approaches in treating Parkinson's disease by reactivating preserved normal memory, and in treating hyperkinetic movement disorders such as chorea or tics by erasing aberrant motor memories.

Intracellular Lactate Dynamics Reveal the Metabolic Diversity of Drosophila Glutamatergic Neurons
Lactate, an intermediary between glycolysis and mitochondrial oxidative phosphorylation, reflects the metabolic state of neurons. Here, we utilized a genetically-encoded lactate FRET biosensor to uncover subpopulations of distinct metabolic states among Drosophila glutamatergic neurons. Neurons within specific subpopulations exhibited correlated lactate flux patterns that stemmed from inherent cellular properties rather than neuronal interconnectivity. Further, individual neurons exhibited consistent patterns of lactate flux over time such that stimulus-evoked changes in lactate were correlated with pre-treatment fluctuations. Leveraging these temporal autocorrelations, deep-learning models accurately predicted post-stimulus responses from pre-stimulus fluctuations. These findings point to the existence of distinct neuronal subpopulations, each characterized by unique lactate dynamics, and raise the possibility that neurons with correlated metabolic activities might synchronize across different neural circuits. Such synchronization, rooted in neuronal metabolic states, could influence information processing in the brain.

Neural Manifold Capacity Captures Representation Geometry, Correlations, and Task-Efficiency Across Species and Behaviors
The study of the brain encompasses multiple scales, including temporal, spatial, and functional aspects. To integrate understanding across these different levels and modalities, it requires developing quantification methods and frameworks. Here, we present effective Geometric measures from Correlated Manifold Capacity theory (GCMC) for probing the functional structure in neural representations. We utilize a statistical physics approach to establish analytical connections between neural co-variabilities and downstream read-out efficiency. These effective geometric measures capture both stimulus-driven and behavior-driven structures in neural population activities, while extracting computationally-relevant information from neural data into intuitive and interpretable analysis descriptors. We apply GCMC to a diverse collection of datasets with different recording methods, various model organisms, and multiple task modalities. Specifically, we demonstrate that GCMC enables a wide range of multi-scale data analysis. This includes quantifying the spatial progression of encoding efficiency across brain regions, revealing the temporal dynamics of task-relevant manifold geometry in information processing, and characterizing variances as well as invariances in neural representations throughout learning. Lastly, the effective manifold geometric measures may be viewed as order parameters for phases related to computational efficiency, facilitating data-driven hypothesis generation and latent embedding.

Inhibition of Protein Synthesis Attenuates Formation of Traumatic Memory and Normalizes Fear-induced c-Fos Expression in a Mouse Model of Posttraumatic Stress Disorder
Posttraumatic stress disorder (PTSD) is a debilitating psychosomatic condition characterized by impairment of brain fear circuits and persistence of exceptionally strong associative memories notoriously resistant to extinction. In this study, we investigated the neural and behavioral consequences of inhibiting protein synthesis, a process known to suppress the formation of conventional aversive memories, in an established animal model of PTSD based on contextual fear conditioning in mice. Control animals were subjected to the conventional fear conditioning task to evaluate the differential impact of protein synthesis inhibition on traumatic versus normal aversive memories. Utilizing c-Fos neural activity mapping, we found that the retrieval of PTSD and normal aversive memories produced activation of overlapping set of brain structures, though several specific areas, such as the infralimbic cortex and the paraventricular nucleus of the thalamus, exhibited heightened activation during induction of PTSD. Administration of protein synthesis inhibitor cycloheximide prior to PTSD induction disrupted the formation of traumatic memories, resulting in behavior that matched the behavior of mice with usual aversive memory. Concomitant with this behavioral shift was a normalization of brain c-Fos activation pattern matching the one observed in usual fear memory. Our findings demonstrate that inhibiting protein synthesis during traumatic experiences significantly impairs the development of PTSD in a mouse model. These data provide insights into the neural underpinnings of protein synthesis-dependent traumatic memory formation and open prospects for the development of new therapeutic strategies for prevention of PTSD.

Chemically induced senescence prompts functional changes in human microglia-like cells
In response to various stressors, cells can enter a state called cellular senescence which is characterized by irreversible cell cycle arrest and a senescence-associated secretory phenotype (SASP). The progressive accumulation of senescent glial cells in the central nervous system (CNS) with aging suggests a potential role for senescence as driver of aging and inflammation in the brain. As the main immune cell population residing in the CNS, microglia are thought to play a pivotal role in the progression of age-associated neuroinflammation. Furthermore, due to their slow turnover, microglia are highly susceptible to undergoing cellular senescence. However, current understanding of age-related changes in microglia and their impact on brain aging is limited. Due to the challenge in accessing human primary microglia and the lack of models to adequately recapitulate aging, this knowledge is predominantly limited to rodent studies. Here, we chemically induced senescence in a human immortalized microglia cell line with a cocktail of senescence inducing molecules. We demonstrate that chemically induced senescent microglia adopt a pro-inflammatory phenotype, have reduced phagocytic activity and impaired calcium activity. Our results show that chemically induced senescence can mimic features of cellular aging and can provide insight on the impact of aging and cellular senescence on human microglia.

Imaging brain vascular function in Cystic Fibrosis: an MRI study of cerebral blood flow and brain oxygenation
Cystic fibrosis (CF) is a progressive inherited disorder that primarily affects the lungs. With recent breakthroughs in effective treatments for CF that increase life-expectancy, a higher prevalence of age-related comorbidities have been reported including cardiovascular disease, stroke and cognitive decline. Despite the known relationship between cardiovascular health and cerebrovascular function, very little is known about brain blood flow and oxygen metabolism in patients with CF (PwCF). In 14 PwCF and 56 healthy age / sex matched controls, we used pseudo-continuous arterial spin labelling (pCASL) to quantify cerebral perfusion in grey-matter and T2-Relaxation-Under-Spin-Tagging (TRUST) to estimate global oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2). Compared to healthy controls, PwCF showed elevated CMRO2 (p = 0.015). There were no significant between-group differences in grey-matter CBF (p = 0.342), or whole brain OEF (p = 0.091). However, regional analysis showed certain areas with higher CBF in PwCF (p < .05, FDR). This is the first study to characterise cerebrovascular function and brain oxygen metabolism in PwCF. Our findings highlight the need for early cardiovascular monitoring procedures to help maintain cerebrovascular function and combat accelerated aging effects in the brains of PwCF.

Effects of prenatal maternal immune activation and exposure to circadian disruption during adolescence: exploring the two-hit model of neurodevelopmental disorders
Background: Around 80% of individuals with neurodevelopmental disorders (NDDs) such as schizophrenia and autism spectrum disorders experience disruptions in sleep/circadian rhythms. We explored whether prenatal infection, an established risk factor for NDDs, and environmental circadian disruption synergistically induced sex-specific deficits in mice. Methods: A maternal immune activation (MIA) protocol was used by injecting pregnant mice (at E9.5) with a viral mimic poly IC or saline. Then, juvenile/adolescent offspring (3-7 weeks old) were subjected to either standard lighting (12:12LD) or constant light (LL). Results: We found interactions of the two factors on behaviors related to cognition, anxiety, and sociability. Also, poly IC exposure led to a more activated profile of hippocampal microglia in males only, while LL diminished these effects. Using RNA sequencing in the dorsal hippocampus, we found that poly IC exposure led to many differentially expressed genes in males (but not females), and fewer differentially expressed genes were observed after LL exposure. Using the WGCNA analysis, we found several significant gene modules positively associated with poly IC (in comparison to saline exposure) and LL (in comparison to LD exposure) in males, and less so in females. Interestingly, many of the identified hub bottleneck genes were homologous to human genes associated with both sleep/circadian rhythms and neurodevelopmental disorders as identified by GWA studies. Conclusions: Our work demonstrates that in a mouse model of prenatal infection, disruptions in circadian rhythms induced by LL play a role in modulating the effects of MIA at behavioral, cellular, and molecular levels.

Pre-existing visual responses in a projection-defined dopamine population explain individual learning trajectories
Learning a new task is challenging because the world is high dimensional, with only a subset of features being reward-relevant. What neural mechanisms contribute to initial task acquisition, and why do some individuals learn a new task much more quickly than others? To address these questions, we recorded longitudinally from dopamine (DA) axon terminals in mice learning a visual task. Across striatum, DA responses tracked idiosyncratic and side-specific learning trajectories. However, even before any rewards were delivered, contralateral-side-specific visual responses were present in DA terminals only in the dorsomedial striatum (DMS). These pre-existing responses predicted the extent of learning for contralateral stimuli. Moreover, activation of these terminals improved contralateral performance. Thus, the initial conditions of a projection-specific and feature-specific DA signal help explain individual learning trajectories. More broadly, this work implies that functional heterogeneity across DA projections serves to bias target regions towards learning about different subsets of task features, providing a mechanism to address the dimensionality of the initial task learning problem.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components
Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method--the choice-wide behavioral association study: CBAS--that systematically identifies such behavioral features. CBAS uses powerful, resampling-based, methods of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Cyclase-associated protein 1 (CAP1) represses MRTF-SRF-dependent gene expression in the mouse cerebral cortex
Serum response factor (SRF) is a ubiquitously expressed transcription factor essential for brain development and function. SRF activity is controlled by two competing classes of coactivators, myocardin-related transcription factors (MRTF) and ternary complex factors (TCF), which introduce specificity into gene expression programs. To date, only few brain studies investigated upstream regulatory mechanisms, which mainly focused on TCF. Since an inhibitory function of monomeric actin towards MRTF-SRF signaling is well-established, we hypothesized a regulatory role for the key actin regulator ADF/cofilin. Surprisingly, ADF/cofilin was largely dispensable for neuronal MRTF-SRF activity. Instead, reporter assays combined with pharmacological and genetic approaches in isolated mouse neurons identified cyclase-associated protein 1 (CAP1) as an important regulator of this pathway. CAP1 promotes cytosolic MRTF retention and represses neuronal MRTF-SRF signaling via an actin-dependent mechanism that requires two specific protein domains. Further, deep RNA sequencing and mass spectrometry in mutant mice proved CAP1's in vivo relevance for this pathway in the cerebral cortex, and led to the identification of neuronal MRTF-SRF target genes. Together, we identified CAP1 as a novel and crucial repressor of neuronal MRTF-SRF signaling.

HB-EGF and EGF infusion following CNS demyelination mitigates age-related decline in regeneration of oligodendrocytes from neural precursor cells originating in the ventricular-subventricular zone
In multiple sclerosis (MS), chronic demyelination initiated by immune-mediated destruction of myelin, leads to axonal damage and neuronal cell death, resulting in a progressive decline in neurological function. The development of interventions that potentiate remyelination could hold promise as a novel treatment strategy for MS. To this end, our group has demonstrated that neural precursor cells (NPCs) residing in the ventricular-subventricular zone (V-SVZ) of the adult mouse brain contribute significantly to remyelination in response to central nervous system (CNS) demyelination and can regenerate myelin of normal thickness. However, aging takes its toll on the regenerative potential of NPCs and reduces their contribution to remyelination. In this study, we investigated how aging influences the contribution of NPCs to oligodendrogenesis during the remyelination process and whether the delivery of growth factors into the brains of aged mice could potentiate the oligodendrogenic potential of NPCs. To enable us to map the fate of NPCs in response to demyelination induced at different postnatal ages, Nestin-CreERT2; Rosa26-LSL-eYFP mice were gavaged with tamoxifen at either 8 weeks, 30 weeks or one year of age before being challenged with cuprizone for a period of six weeks. Using osmotic minipumps, we infused heparin-binding EGF-like growth factor (HB-EGF), and/or epidermal growth factor (EGF) into the cisterna magna for a period of two weeks beginning at the peak of cuprizone-induced demyelination (n=6-8 mice per group). Control mice received artificial cerebrospinal fluid (vehicle) alone. Mice were perfused six weeks after cuprizone withdrawal and the contribution of NPCs to oligodendrocyte regeneration in the corpus callosum was assessed. Our data reveal that although NPC-derived oligodendrocyte generation declined dramatically with age, this decline was partially reversed by growth factor infusion. Notably, co-infusion of EGF and HB-EGF increased oligodendrocyte regeneration twofold in some regions of the corpus callosum. Our results shed light on the beneficial effects of EGF and HB-EGF for increasing the contribution of NPCs to remyelination and indicate their therapeutic potential to combat the negative effects of aging upon remyelination efficacy.

Alpha-synuclein aggregates trigger anti-viral immune pathways and RNA editing in human astrocytes
Parkinson's disease is a neurodegenerative disease characterised by a proteinopathy with marked astrogliosis. To investigate how a proteinopathy may induce a reactive astrocyte state, and the consequence of reactive astrocytic states on neurons, we generated hiPSC-derived astrocytes, neurons and co-cultures and exposed them to small soluble alpha-synuclein aggregates. Oligomeric alpha-synuclein triggered an inflammatory state associated with TLR activation, viral responses, and cytokine secretion. This reactive state resulted in loss of neurosupportive functions, and the induction of neuronal toxicity. Notably, interferon response pathways were activated leading to upregulation, and isoform switching of the RNA deaminase enzyme, ADAR1. ADAR1 mediates A-to-I RNA editing, and increases in RNA editing were observed in inflammatory pathways in cells, as well as in post-mortem human PD brain. Aberrant, or dysregulated, ADAR1 responses and RNA editing may lead to sustained inflammatory reactive states in astrocytes triggered by alpha-synuclein aggregation, and this may drive the neuroinflammatory cascade in Parkinson's.

The 5-HT7 receptor antagonist SB 269970 ameliorates maternal fluoxetine exposure-induced impairment of synaptic plasticity in the prefrontal cortex of the offspring female mice
Background Although the use of a selective serotonin reuptake inhibitor fluoxetine in depression during pregnancy is considered safe, it might increase the risk of affective disorders and cognitive symptoms in progeny. In animal models, maternal exposure to fluoxetine throughout gestation and lactation negatively affects the behavior of the offspring. Little is known about the effects of maternal fluoxetine on synaptic transmission and plasticity in the offspring cerebral cortex. Methods During pregnancy and lactation C57BL/6J mouse dams received fluoxetine (7.5 mg/kg/day) with drinking water. Female offspring mice received intraperitoneal injections of the selective 5-HT7 receptor antagonist SB 269970 (2.5 mg/kg) for 7 days. Whole-cell and field potential electrophysiological recordings were performed in the medial prefrontal cortex (mPFC) ex vivo brain slices. Results Perinatal exposure to fluoxetine resulted in decreased field potentials and impaired long-term potentiation (LTP) in layer II/III of the mPFC of female young adult offspring. Neither the intrinsic excitability nor spontaneous excitatory postsynaptic currents were altered in layer II/III mPFC pyramidal neurons. In mPFC slices obtained from fluoxetine-treated mice that were administered SB 269970 both field potentials and LTP magnitude were restored and did not differ from controls. Conclusions Treatment of fluoxetine-exposed mice with a selective 5-HT7 receptor antagonist, SB269970, normalizes synaptic transmission and restores the potential for plasticity in the mPFC of mice exposed in utero and postnatally to fluoxetine.

Computational joint action: dynamical models to understand the development of joint coordination
Coordinating with others is part of our everyday experience. Previous studies using sensorimotor coordination games suggest that human dyads develop coordination strategies that can be interpreted as Nash equilibria. However, if the players are uncertain about what their partner is doing, they develop coordination strategies which are robust to the actual partner's actions. This has suggested that humans select their actions based on an explicit prediction of what the partner will be doing -- a partner model -- which is probabilistic by nature. However, the mechanisms underlying the development of a joint coordination over repeated trials remain unknown. Very much like sensorimotor adaptation of individuals to external perturbations (eg force fields or visual rotations), dynamical models may help to understand how joint coordination develops over repeated trials. Here we present a general computational model -- based on game theory and Bayesian estimation -- designed to understand the mechanisms underlying the development of a joint coordination over repeated trials. Joint tasks are modeled as quadratic games, where each participant's task is expressed as a quadratic cost function. Each participant predicts their partner's next move (partner model) by optimally combining predictions and sensory observations, and selects their actions through a stochastic optimization of its expected cost, given the partner model. The model parameters include perceptual uncertainty (sensory noise), partner representation (retention rate and process noise), uncertainty in action selection and its rate of decay (which can be interpreted as the action's learning rate). The model can be used in two ways: (i) to simulate interactive behaviors, thus helping to make specific predictions in the context of a given joint action scenario; and (ii) to analyze the action time series in actual experiments, thus providing quantitative metrics that describe individual behaviors during an actual joint action. We demonstrate the model in a variety of joint action scenarios. In a sensorimotor version of the Stag Hunt game, the model predicts that different representations of the partner lead to different Nash equilibria. In a joint two via-point (2-VP) reaching task, in which the actions consist of complex trajectories, the model captures well the observed temporal evolution of performance. For this task we also estimated the model parameters from experimental observations, which provided a comprehensive characterization of individual dyad participants. Computational models of joint action may help identify the factors preventing or facilitating the development of coordination. They can be used in clinical settings, to interpret the observed behaviors in individuals with impaired interaction capabilities. They may also provide a theoretical basis to devise artificial agents that establish forms of coordination that facilitate neuromotor recovery.

Disentangling Mnemonic Metacognition from Confidence by Communication between the Precuneus and Hippocampus
Metacognition, the ability to introspectively monitor confidence prior to decision-making, remains an area with unclear neural mechanisms, particularly concerning the intercommunication among various brain regions. Recently, the precuneus has emerged as a key player in mnemonic metacognitive tasks, while the hippocampus's involvement in memory function is well-established. Our study sought to examine the roles of the precuneus and hippocampus in mnemonic metacognition, employing intracranial electrode recordings from patients with intractable epilepsy to analyze the regulatory and correlational dynamics between these regions. We identified distinct communication patterns between the precuneus and hippocampus during metacognition and confidence generation. Furthermore, we conducted a quantitative assessment of the temporal dynamics involved in metacognition and the generation of confidence. Our observations reveal that introspective judgments typically occur subsequent to the generation of confidence. Our study sheds light on the neural underpinnings of metacognition and the genesis of confidence, focusing on the interplay between the hippocampus and the precuneus. This investigation lays the groundwork for deepening our comprehension of the nuances distinguishing confidence from metacognition.

Hippocampal DNA methylation promotes memory persistence by facilitating systems consolidation and cortical engram stabilisation.
The long-term stabilization of memory traces or engram involves the rapid formation of cortical engrams during encoding that mature functionally over time guided by the activity of the hippocampus. The molecular mechanisms that regulate this process remain largely unknown. Here, we found that hippocampal DNA methylation converts short-lasting into long-lasting memories by promoting systems consolidation and the stabilization of cortical engrams.

Evidence for shallow cognitive maps in schizophrenia
Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could also arise when goal-directed control and motivation are intact, but used to plan over ill-formed maps. Here, we test the hypothesis that individuals with SZ are impaired in the construction of cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that SZ were able to display similar performance on this task compared to controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.

A parabrachial hub for the prioritization of survival behavior
Long-term sustained pain in the absence of acute physical injury is a prominent feature of chronic pain conditions. While neurons responding to noxious stimuli have been identified, understanding the signals that persist without ongoing painful stimuli remains a challenge. Using an ethological approach based on the prioritization of adaptive survival behaviors, we determined that neuropeptide Y (NPY) signaling from multiple sources converges on parabrachial neurons expressing the NPY Y1 receptor to reduce sustained pain responses. Neural activity recordings and computational modeling demonstrate that activity in Y1R parabrachial neurons is elevated following injury, predicts functional coping behavior, and is inhibited by competing survival needs. Taken together, our findings suggest that parabrachial Y1 receptor-expressing neurons are a critical hub for endogenous analgesic pathways that suppress sustained pain states.

The DREAM implant: A Lightweight, Modular and Cost-Effective Implant System for Chronic Electrophysiology in Head-fixed and Freely Behaving Mice
Chronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for implanting electrodes chronically present steep trade-offs between cost, ease of use, size, adaptability and long-term stability. This protocol introduces a novel chronic electrode implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps, and explanted safely for recovery and multiple re-use of probes, significantly reducing experimental costs. The DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7mm; (2) a 3D-printable, open-source design for a wearable Faraday cage covered in copper mesh for electrical shielding, impact protection and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 hours), probe safety and animal welfare. The resulting implants had minimal impact on animals' behavioral repertoire, were easily applicable in freely moving and head-fixed contexts, and delivered clearly identifiable spike waveforms and healthy neuronal responses for several months post-implant. Infections and other surgery complications were extremely rare using this protocol. As such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being and enabling more ethologically sound experiments. Its design simplifies experimental procedures and is adaptable to various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.

A model for cortical activity sequences
Networks of neurons in the brain, that act on a timescale of milliseconds, can intrinsically generate reliable sequential activity on slow behavioral timescales of seconds. A possible mechanism for intrinsic sequence generation based on theoretical evidence points to distance-dependent connectivity with correlated spatial asymmetries, establishing an anisotropic network connectivity. We show that networks with such correlated asymmetric connectivity as well as symmetric distance-dependent connectivity match experimental data of connectivity motifs as well as neuronal activity statistics from rat and monkey cortex. At the network level, however, only the correlated asymmetric connectivity pattern generates spatiotemporal activity sequences on behaviorally relevant timescales, while the symmetric connectivity results in transient but stationary spatial bumps of neural activity. Our results strongly support the role of correlated asymmetries in connectivity for the generation of sequential activity in neural networks.

in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning
The rising quality and amount of multi-omic data across biomedical science demands that we build innovative solutions to harness their collective discovery potential. From publicly available repositories, we have assembled and curated a compendium of gene-level transcriptomic data focused on mammalian excitatory neurogenesis in the neocortex. This collection is open for exploration by both computational and cell biologists at nemoanalytics.org, and this report forms a demonstration of its utility. Applying our novel structured joint decomposition approach to mouse, macaque and human data from the collection, we define transcriptome dynamics that are conserved across mammalian excitatory neurogenesis and which map onto the genetics of human brain structure and disease. Leveraging additional data within NeMO Analytics via projection methods, we chart the dynamics of these fundamental molecular elements of neurogenesis across developmental time and space and into postnatal life. Reversing the direction of our investigation, we use transcriptomic data from laminar-specific dissection of adult human neocortex to define molecular signatures specific to excitatory neuronal cell types resident in individual layers of the mature neocortex, and trace their emergence across development. We show that while many lineage defining transcription factors are most highly expressed at early fetal ages, the laminar neuronal identities which they drive take years to decades to reach full maturity. Finally, we interrogated data from stem-cell derived cerebral organoid systems demonstrating that many fundamental elements of in vivo development are recapitulated with high-fidelity in vitro, while specific transcriptomic programs in neuronal maturation are absent. We propose these analyses as specific applications of the general approach of combining joint decomposition with large curated collections of analysis-ready multi-omics data matrices focused on particular cell and disease contexts. Importantly, these open environments are accessible to, and must be fueled with emerging data by, cell biologists with and without coding expertise.

Supplementary motor area disinhibition during motor sequence learning: A TMS-EEG study
Background: In primary motor cortex, changes in excitatory and inhibitory neurotransmission (E:I balance) accompany motor sequence learning. In particular, there is an early reduction in inhibition (i.e., disinhibition). The supplementary motor area (SMA) is a key brain region involved in the learning of sequences, however the neurophysiological mechanisms within SMA which support motor sequence learning remain poorly understood. Disinhibition may also occur in SMA, but this possibility remains unexamined. Objective: We investigated disinhibition within SMA during motor sequence learning using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Methods: Twenty-nine healthy adults practiced a sequential motor task. TMS-evoked potentials (TEPs) resulting from SMA stimulation were measured with EEG before, during, and after practice. The N45 TEP peak was our primary measure of disinhibition. Furthermore, the slope of aperiodic EEG activity was included as an additional E:I balance measure. Results: Significant improvements in task performance (i.e., learning) occurred with practice. We observed smaller N45 amplitudes during early learning relative to baseline (both p < .01), indicative of disinhibition. Intriguingly, aperiodic exponents increased as learning progressed and were associated with greater sequence learning (p < .05). Conclusion: Our results show disinhibition within SMA during the planning phase of motor sequence learning and thus provide novel understanding on the neurophysiological mechanisms within higher-order motor cortex that accompany new sequence learning.

Blind individuals' enhanced ability to sense their own heartbeat is related to the thickness of their occipital cortex
Blindness is associated with heightened sensory abilities, such as improved hearing and tactile acuity. Moreover, recent evidence suggests that blind individuals are better than sighted individuals at perceiving their own heartbeat, suggesting enhanced interoceptive accuracy. Structural changes in the occipital cortex have been hypothesized as the basis of these behavioral enhancements. Indeed, several studies have shown that congenitally blind individuals have increased cortical thickness within occipital areas compared to sighted individuals, but how these structural differences relate to behavioral enhancements is unclear. This study investigated the relationship between cardiac interoceptive accuracy and cortical thickness in 23 congenitally blind individuals and 23 matched sighted controls. Our results show a significant positive correlation between performance in a heartbeat counting task and cortical thickness only in the blind group, indicating a connection between structural changes in occipital areas and blind individuals' better ability to perceive heartbeats.

Stable sequential dynamics in prefrontal cortex represents subjective estimation of time
Time estimation is an essential prerequisite underlying various cognitive functions. Previous studies identified sequential firing and activity ramps as the primary neuron activity patterns in the medial frontal cortex (mPFC) that could convey information regarding time. However, the relationship between these patterns and the timing behavior has not been fully understood. In this study, we utilized in vivo calcium imaging of mPFC in rats performing a timing task. By aligning long-term time-lapse datasets, we discovered that sequential patterns of time coding were stable over weeks, while cells with ramping activity patterns showed constant dynamism. Furthermore, with a novel behavior design that allowed the animal to determine individual trial interval, we were able to demonstrate that real-time adjustment in the sequence procession speed closely tracked the trial-to-trial interval variations. And errors in the rats timing behavior can be primarily attributed to the premature ending of the time sequence. Together, our data suggest that sequential activity might be a more relevent coding regime than the ramping activity in representing time under physiological conditions. Furthermore, our results imply the existence of a unique cell type in the mPFC that participates in the time-related sequences. Future characterization of this cell type could provide important insights in the neural mechanism of timing and related cognitive functions.

Perinatal Brain Injury Triggers Niche-Specific Changes to Cellular Biogeography
Preterm infants are at risk for brain injury and neurodevelopmental impairment due, in part, to white matter injury following chronic hypoxia exposure. However, the precise molecular mechanisms by which perinatal hypoxia disrupts early neurodevelopment are poorly understood. Here, we constructed a brain-wide map of the regenerative response to newborn brain injury using high-resolution imaging-based spatial transcriptomics to analyze over 1.3 million cells in a mouse model of chronic neonatal hypoxia. Additionally, we developed a new method for inferring condition-associated differences in cell type spatial proximity, enabling the identification of niche-specific changes in cellular architecture. We observed hypoxia-associated changes in region-specific cell states, cell type composition, and spatial organization. Importantly, our analysis revealed mechanisms underlying reparative neurogenesis and gliogenesis, while also nominating pathways that may impede circuit rewiring following perinatal hypoxia. Altogether, our work provides a comprehensive description of the molecular response to newborn brain injury.

Electrophysiological dynamics of cognitive control networks in human memory and replication across four experiments
Dynamic interactions between large-scale brain networks are thought to underpin human cognitive processes such as episodic memory formation, but their underlying electrophysiological dynamics are not known. The triple network model, highlighting the salience, default mode, and frontoparietal networks, are fundamental to this process. To unravel the electrophysiological mechanisms underlying these interactions, we utilized intracranial EEG from 177 participants across four memory experiments. Findings revealed directed information flow from the anterior insula node of the salience network to the default mode and frontoparietal networks, regardless of the nature of the tasks - whether they involved externally driven stimuli during encoding or internally governed processes during free recall. Moreover, this pattern of information transmission was observed irrespective of the activation or suppression states of network nodes. Crucially, results were replicated across four different memory experiments. Our study advances understanding of how coordinated neural network interactions underpin cognitive operations.

Failed stopping transiently suppresses the electromyogram in task-irrelevant muscles
Selectively stopping individual parts of planned or ongoing movements is an everyday motor skill. For example, while walking in public you may stop yourself from waving at a stranger who you mistook for a friend while continuing to walk. Despite its ubiquity, our ability to selectively stop actions is limited. Canceling one action can delay the execution of other simultaneous actions. This stopping-interference effect on continuing actions during selective stopping may be attributed to a global inhibitory mechanism with widespread effects on the motor system. Previous studies have characterized a transient global reduction in corticomotor excitability by combining brain stimulation with electromyography (EMG). Here, we examined whether global motor inhibition during selective stopping can be measured peripherally and with high temporal resolution using EMG alone. Eighteen participants performed a bimanual anticipatory response inhibition task with their index fingers while maintaining a tonic contraction of the task-irrelevant abductor digiti minimi (ADM) muscles. A time series analysis of the ADM EMG signal revealed transient inhibition during failed stopping compared to go response trials 150 ms to 203 ms following the stop signal. The pattern was observed in both hands during bimanual stop-all trials as well as selective stop-left and stop-right trials of either hand. These results indicate that tonic muscle activity is sensitive to the effects of global motor suppression even when stopping fails. Therefore, EMG can provide a physiological marker of global motor inhibition to probe the time course and extent of stopping processes.

A Drug Screening Platform for Protein Expression Levels in Neurological Disorders
Neurological and psychiatric diseases and disorders affect more than half of the population. Many of these diseases are caused by the malfunctioning of protein synthesis, where too little or too much production of a protein harms a cell and its functions within the brain. We developed a drug screening platform to identify compounds that target the primary cause of these diseases, namely protein expression amounts. This cellular assay monitors protein expression of a target disease gene along with the protein expression of a control gene using the Protein Quantitation Ratioing (PQR) technique. PQR tracks protein concentration using fluorescence. We used human cells and CRISPR-Cas9 genome editing to insert the Protein Quantitation Reporter into target genes. These cells are used in high-throughput drug screening measuring the fluorescence as the assay. Drug hits can be validated using the same PQR technique or animal models of the disease.

L-Type calcium channels and TRPC3 channels shape brain pericyte calcium signaling and hemodynamics throughout the arteriole to capillary network in vivo
Pericytes play a crucial role in regulating cerebral blood flow (CBF) through processes like vasomotion and neurovascular coupling (NVC). Recent work has identified different pericyte types at distinct points in the cerebrovascular network, such as the arteriole-capillary transition zone (ACT) and distal capillaries, sparking debate about their functional roles in blood flow control. Part of this discussion has comprised the possible mechanisms that may regulate pericyte Ca2+ signaling. Using in vivo two-photon Ca2+ imaging and a pharmacological approach with Ca2+ channel blockers (nimodipine and Pyr3), we assessed the contribution of L-type voltage-gated Ca2+ channels (VGCC) and transient receptor potential canonical 3 (TRPC3) channels to Ca2+ signaling in different pericyte types, ensheathing and capillary pericytes. We also measured local hemodynamics such as vessel diameter, blood cell velocity and flux during vasomotion, and following somatosensory stimulation to evoke NVC. We report that VGCC and TRPC3 channels underlie spontaneous fluctuations in ensheathing pericyte Ca2+ that trigger vasomotor contractions, but the contribution of each of these mechanisms to vascular tone depends on the specific branch of the ACT. Distal capillary pericytes also express L-type VGCCs and TRPC3 channels and they mediate spontaneous Ca2+ signaling in these cells. However, only TRPC3 channels maintain resting capillary tone, possibly by a receptor-operated Ca2+ entry mechanism. By applying the Ca2+ channel blockers during NVC, we found a significant involvement of L-type VGCCs in both pericyte types, influencing their ability to dilate during functional hyperemia. These findings provide new evidence of VGCC and TRPC3 activity in pericytes in vivo and establish a clear distinction between brain pericyte types and their functional roles, opening avenues for innovative strategies to selectively target their Ca2+ dynamics for CBF control.

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning.
Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step, reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

Hippocampal purinergic P2X7 receptor level is increased in Alzheimer's disease patients, and associated with amyloid and tau pathologies
INTRODUCTION: The purinergic receptor P2X7R, which is expressed on microglia and astrocytes, plays an important role in Alzheimer's disease (AD). We aimed to characterize the alterations in P2X7R expression in AD patients by APOE {epsilon}4 allele, age and sex, as well as its association with amyloid and tau pathology. METHODS: P2X7R staining and quantitative analysis of amyloid, tau, astrocytes and microglia were performed on postmortem hippocampal tissues from 35 AD patients; 31 nondemented controls; caudate/putamen tissue from corticobasal degeneration (CBD), progressive supranuclear palsy (PSP) patients; and bran tissue from aged 3xTg mouse model of AD. RESULTS: Activated microglia and reactive astrocytes were observed in the hippocampi of AD patients and exhibited altered morphology with denser cells and pronounced ramifications. Hippocampal P2X7R intensity was greater in the hippocampal subfields of AD patients than in those of nondemented controls and was correlated with amyloid level and Braak stage and was not affected by sex, APOE {epsilon}4 allele, or age. P2X7R expression increased around A{beta} plaques, cerebral amyloid angiopathy, tau inclusions in the hippocampus from AD patients and tau inclusions in the caudate/putamen from CBD and PSP patients. DISCUSSION: We found an increased hippocampal P2X7R level in AD compared to non-demented control, which correlated with amyloid and tau pathologies. P2X7R is a potential marker for neuroinflammation in AD.

Using a Foerster-resonance energy transfer (FRET)-based detection system (FedEcs) to monitor nanoparticle cargo delivery to the brain
Nanotechnology holds great promise to improve delivery of therapeutics to the brain. Current experimental approaches are, however, hampered by the lack of tools to dynamically monitor cargo delivery in vivo. We developed highly fluorescent lipid nanodroplets (LNDs) that carry a Foerster-resonance energy transfer (FRET)-based drug delivery detection system able to monitor cargo release (FedEcs) in vivo. We investigated the distribution, stability, and cargo release of FedEcs-LNDs in the healthy and ischemic mouse brain by intravital multiphoton microscopy. We dynamically observed that LNDs accumulated within cerebral microclots after ischemia, caused by magnetic nanoparticles (Nano-stroke), and released their cargo. Furthermore, the blood-brain barrier (BBB) became permeable at sites of microclots thereby allowing FedEcs-LNDs to cross the BBB and to deliver their cargo to the brain parenchyma. Consequently, FedEcs represents a novel tool to quantitatively investigate the nanocarriers biodistribution and cargo release using intravital microscopy and may thus tremendously ease their translational validation.

Diet-induced metabolic and immune impairments are sex-specifically modulated by soluble TNF signaling in the 5xFAD mouse model of Alzheimers disease
Emerging evidence indicates that high-fat, high carbohydrate diet (HFHC) impacts central pathological features of Alzheimers disease (AD) across both human incidences and animal models. However, the mechanisms underlying this association are poorly understood. Here, we identify compartment-specific metabolic and inflammatory dysregulations that are induced by HFHC diet in the 5xFAD mouse model of AD pathology. We observe that both male and female 5xFAD mice display exacerbated adiposity, cholesterolemia, and dysregulated insulin signaling. Independent of biological sex, HFHC diet also resulted in altered inflammatory cytokine profiles across the gastrointestinal, circulating, and central nervous systems (CNS) compartments demonstrating region-specific impacts of metabolic inflammation. In male mice, we note that HFHC triggered increases in amyloid beta, an observation not seen in female mice. Interestingly, inhibiting the inflammatory cytokine, soluble tumor necrosis factor (TNF) with the brain-permeant soluble TNF inhibitor XPro1595 was able to restore aspects of HFHC-induced metabolic inflammation, but only in male mice. Targeted transcriptomics of CNS regions revealed that inhibition of soluble TNF was sufficient to alter expression of hippocampal and cortical genes associated with beneficial immune and metabolic responses. Collectively, these results suggest that HFHC diet impairs metabolic and inflammatory pathways in an AD-relevant genotype and that soluble TNF has sex-dependent roles in modulating these pathways across anatomical compartments. Modulation of energy homeostasis and inflammation may provide new therapeutic avenues for AD.

GLP-1 receptor agonism ameliorates Parkinsons disease through modulation of neuronal insulin signalling and glial suppression
Neuronal insulin resistance is linked to the pathogenesis of Parkinsons disease through unclear, but potentially targetable, mechanisms. We delineated neuronal and glial mechanisms of insulin resistance and glucagon-like 1 peptide (GLP-1) receptor agonism in human iPSC models of synucleinopathy, and corroborated our findings in patient samples from a Phase 2 trial of a GLP-1R agonist in Parkinsons (NCT01971242). Human iPSC models of synucleinopathy exhibit neuronal insulin resistance and dysfunctional insulin signalling, which is associated with inhibition of the neuroprotective Akt pathways, and increased expression of the MAPK-associated p38 and JNK stress pathways. Ultimately, this imbalance is associated with cellular stress, impaired proteostasis, accumulation of -synuclein, and neuronal loss. The GLP-1R agonist exenatide led to restoration of insulin signalling, associated with restoration of Akt signalling and suppression of the MAPK pathways in neurons. GLP-1R agonism reverses the neuronal toxicity associated with the synucleinopathy, through reduction of oxidative stress, improved mitochondrial and lysosomal function, reduced aggregation of alpha-synuclein, and enhanced neuronal viability. GLP-1R agonism further suppresses synuclein induced inflammatory states in glia, leading to neuroprotection through non cell autonomous effects. In the exenatide-PD2 clinical trial, exenatide treatment was associated with clinical improvement in individuals with higher baseline MAPK expression (and thus insulin resistance). Exenatide treatment led to a reduction of alpha-synuclein aggregates, and a reduction in inflammatory cytokine IL-6. Taken together, our patient platform defines the mechanisms of GLP-1R action in neurons and astrocytes, identifies the population likely to benefit from GLP-1R agonism, and highlights the utility of GLP-1R agonism as a disease modifying strategy in synucleinopathies.

PhysMAP - interpretable in vivo neuronal cell type identification using multi-modal analysis of electrophysiological data
Cells of different types perform diverse computations and coordinate their activity during sensation, perception, and motor control. While electrophysiological approaches can measure the activity of many neurons simultaneously, assigning cell type labels to these neurons is an open problem. Here, we develop PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously in an unsupervised manner and obtain an interpretable representation that separates neurons by cell type. PhysMAP is superior to any single electrophysiological modality in identifying neuronal cell types such as excitatory pyramidal, PV+ interneurons, and SOM+ interneurons with high confidence in both juxtacellular and extracellular recordings and from multiple areas of the mouse brain. PhysMAP built on ground truth data can be used for classifying cell types in new and existing electrophysiological datasets, and thus facilitate simultaneous assessment of the coordinated dynamics of multiple neuronal cell types during behavior.

Decoding Motor Excitability in TMS using EEG-Features:An Exploratory Machine Learning Approach
Background: With the burgeoning interest in personalized treatments for brain network disorders, closed-loop transcranial magnetic stimulation (TMS) represents a promising frontier. Relying on the real-time adjustment of stimulation parameters based on brain signal decoding, the success of this approach depends on the identification of precise biomarkers for timing the stimulation optimally. Objective: We aimed to develop and validate a supervised machine learning framework for the individualized prediction of motor excitability states, leveraging a broad spectrum of sensor and source space EEG features. Methods: Our approach integrates multi-scale EEG feature extraction and selection within a nested cross-validation scheme, tested on a cohort of 20 healthy participants. We assessed the framework's performance across different classifiers, feature sets, and experimental protocols to ensure robustness and generalizability. Results: Personalized classifiers demonstrated a statistically significant mean predictive accuracy of 72 {+/-} 11%. Consistent performance across various testing conditions highlighted the sufficiency of sensor-derived features for accurate excitability state predictions. Subtype analysis revealed distinct clusters linked to specific brain regions and oscillatory features as well as the need for a more extensive feature set for effective biomarker identification than conventionally considered. Conclusions: Our machine learning framework effectively identifies predictive biomarkers for motor excitability, holding potential to enhance the efficacy of personalized closed-loop TMS interventions. While the clinical applicability of our findings remains to be validated, the consistent performance across diverse testing conditions and the efficacy of sensor-only features suggest promising avenues for clinical research and wider applications in brain signal classification.

Decoding Music-Evoked Valence and Arousal: Unraveling the Neural Correlates of Naturalistic Music Characteristics through fMRI
Music can convey basic emotions, such as joy and sadness, and more complex ones, such as tenderness or nostalgia. Its effects on emotion regulation and reward have attracted much attention in cognitive and affective neuroscience. Understanding the underlying neural mechanisms of music-evoked emotions could guide the development of novel technological and individually-tuned neurorehabilitation music-based therapies. This study aims to unravel the relationship between the classification of music excerpts regarding perceived affective states and their associated neural correlates, as measured by fMRI. We used valence and arousal to classify both the stimuli and the affective states perceived by the participants. We acquired fMRI data from 20 participants while listening to 96 musical excerpts a priori classified into four quadrants, considering the valence-arousal model. We first characterized the neural correlates resulting from a GLM analysis of the quadrants defined by valence (positive, negative) and arousal (high, low). Our results highlight the role of neocortical regions, most noticeably the music-specific sub-regions of the auditory cortex and thalamus, as well as regions from the reward network such as the amygdala. Using multivoxel activity patterns corresponding to the four quadrants representation of core affect, we were able to create a computational model that decodes the quadrant corresponding to the music with significant accuracy, well above a stringent chance level. We further analyzed a set of musical features using regression analysis and explored how they relate to brain activity in valence-, arousal-, reward-, and auditory-related ROIs. The results emphasize the role of expressive features in emotion-related networks. These results contribute to the definition of a relation between music and the neural substrate of music listening and emotions, which is key to developing novel music-based neurorehabilitation strategies.

Bringing astrocytes into the spotlight of electrical brain stimulation
Astrocytes, primarily viewed only as supportive units, are now emerging as active players in the information processing of the brain. Accumulated evidence supports that the bidirectional communication between astrocytes and neurons maintains complex animal behaviours such as memory formation and decision-making. The lack of characterisation of astrocytic electrophysiology is, in our opinion, associated with the early idea of a passive electrical nature of astrocytes, in opposition to the electrically active neurons. A better understanding of the effect of electrical stimulation on astrocytes' physiology and activity will greatly strengthen the current knowledge in neural biology. Here, we assessed if astrocytes may have a role in therapies based on electrical brain stimulation by being able to respond to the same electrical stimulus used to modulate neuronal activity. To do so, we took advantage of microelectrode arrays (MEAs) capability to simultaneously record and deliver extracellular electrical signals. Additionally, we synchronized the recording of electrophysiological data with the recording of calcium activity, a hallmark of astrocytic activity. Here, we show that astrocytes respond to electrical stimulation with the generation of strong membrane voltage oscillations and simultaneous production of calcium waves, demonstrating, unequivocally, that astrocytes respond to electrical stimulation in the same range as neurons do. Importantly, these responses are dependent on the stimuli amplitude. Furthermore, membrane voltage oscillations are significantly reduced in the absence of extracellular calcium, but not abolished, while calcium activity is not detected.

Agency accounts for the effect of FB transparency onmotor imagery neurofeedback performance
Objective: Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) on the participant's brain activity that they must learn to self-regulate. The visual FB traditionally delivered in a NF task manifests as a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked -from the subject's perspective- to the task performed (e.g., motor imagery). This may decrease the sense of agency, the participants' reported control over FB. We assessed the influence of FB transparency on NF performance and the role of agency in this relationship. Approach: Participants performed a NF task using motor imagery to regulate brain activity measured using electroencephalography. In separate blocks, participants experienced three different conditions designed to vary transparency: FB was presented as either 1) an oscillating pendulum, 2) a clenching virtual hand, 3) a clenching virtual hand combined with a motor illusion induced by tendon vibration. Main results: We found that FB transparency influences NF performance. Transparent visual FB provided by the virtual hand resulted in significantly better NF performance than the abstract FB of the pendulum. Surprisingly, adding a motor illusion to the virtual hand significantly decreased performance relative to the virtual hand alone. We found that self-reported agency was significantly associated with NF performance at the within-subject level across all FB types. Significance: Our results highlight the relevance of transparent FB but also the importance of FB eliciting a strong sense of agency. This factor is likely an important consideration in designing multimodal FB, which while usually recommended, should be tailored to maximize the sense of agency in order to improve NF performance and learning outcomes.

Proportion and distribution of neurotransmitter-defined cell types in the ventral tegmental area and substantia nigra pars compacta
Most studies on the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) have focused on dopamine neurons and their role in processes such as motivation, learning, movement, and associated disorders. However there has been increasing attention on other VTA and SNc cell types that release GABA, glutamate, or a combination of these neurotransmitters. Yet the relative distributions and proportions of neurotransmitter-defined cell types across VTA and SNc has remained unclear. Here, we used fluorescent in situ hybridization in male and female mice to label VTA and SNc neurons that expressed mRNA encoding the canonical vesicular transporters for dopamine, GABA, or glutamate: vesicular monoamine transporter VMAT2, vesicular GABA transporter (VGAT), and vesicular glutamate transporter (VGLUT2). Within VTA, we found that no one type was particularly more abundant, instead we observed similar numbers of VMAT2+ (44%), VGAT+ (37%) and VGLUT2+ (41%) neurons. In SNc we found that a slight majority of neurons expressed VMAT2 (54%), fewer were VGAT+ (42%), and VGLUT2+ neurons were least abundant (16%). Moreover, 20% of VTA neurons and 10% of SNc neurons expressed more than one vesicular transporter, including 45% of VGLUT2 neurons. We also assessed within VTA and SNc subregions and found remarkable heterogeneity in cell-type composition. And by quantifying density across both anterior-posterior and medial-lateral axes we generated heatmaps to visualize the distribution of each cell type. Our data complement recent single-cell RNAseq studies and support a more diverse landscape of neurotransmitter-defined cell types in VTA and SNc than is typically appreciated.

New approaches to the Single-Interval Adjustment Matrix (SIAM) Yes-No task
Two adaptations of the Single-Interval Adjust-Matrix Yes-No (SIAM-YN) task, designed to increase the efficiency of absolute threshold estimation, are described. The first, the SIAM Twin Track (SIAM-TT) task, consists of two interleaved tracks of the standard SIAM-YN that are run in the same trial with a single response. The second new task modifies the binary SIAM-YN task by using a six-point rating-scale (SIAM-Rating). In Experiment 1, data from three tasks estimating absolute thresholds were obtained using a 10-ms tone, the 2-IFC transformed up-down procedure, SIAM-YN task, and the SIAM-TT task. The data support the use of the SIAM-TT as an alternative to the conventional two-interval and one-interval (SIAM-YN) tasks when used to estimate absolute thresholds. By presenting two interleaved SIAM-YN tracks on a single experimental trial, the SIAM-TT task possesses greater efficiency alongside its signal- detection heritage which confers less response bias. Similarly, in Experiment 2, which compared the 2-IFC adaptive, SIAM-YN, and SIAM-Rating tasks, there was no main effect of task upon threshold estimates. The findings replicate previous studies supporting the validity and efficiency of the SIAM-YN task, and extends the SIAM-YN toolbox to efficiently facilitate the generation of psychometric functions (the SIAM-TT task) and Receiver Operating Characteristic Curves (the SIAM-Rating task).

Absence seizures and sleep abnormalities in a rat model of GRIN2B neurodevelopmental disorder
Pathogenic mutations in GRIN2B are an important cause of severe neurodevelopmental disorders resulting in epilepsy, autism and intellectual disability. GRIN2B encodes the GluN2B subunit of N-methyl-D-aspartate receptors (NMDARs), which are ionotropic glutamate receptors critical for normal development of the nervous system and synaptic plasticity. Here, we characterized a novel GRIN2B heterozygous knockout rat model with 24-hour EEG recordings. We found rats heterozygous for the deletion (Grin2b+/-) had a higher incidence of spontaneous absence seizures than wild-type rats (Grin2b+/+). Spike and wave discharges, the electrographic correlate of absences seizures, were longer in duration and displayed increased higher overall spectral power in Grin2b+/- animals than those in Grin2b+/+. Heterozygous mutant rats also had abnormal sleep-wake brain state dynamics over the circadian cycle. Specifically, we identified a reduction in total rapid eye movement sleep and, altered distributions of non-rapid eye movement sleep and wake epochs, when compared to controls. This was accompanied by an increase in overall spectral power during non-rapid eye movement sleep in Grin2b+/-. The sleep-wake phenotypes were largely uncorrelated to the incidence of spike and wave discharges. We then tested the antiseizure efficacy of ethosuximide, a T-type voltage-gated calcium channel blocker used in the treatment of absence seizures, and memantine, a noncompetitive NMDAR antagonist currently explored as a mono or adjunctive treatment option in NMDAR related neurodevelopmental disorders. Ethosuximide reduced the number and duration of spike and wave discharges, while memantine did not affect the number of spike and wave discharges but reduced their duration. These results highlight two potential therapeutic options for GRIN2B related epilepsy. Our data shows the new rat GRIN2B haploinsufficiency model exhibits clinically relevant phenotypes. As such, it could prove crucial in deciphering underlying pathological mechanisms and developing new therapeutically translatable strategies for GRIN2B neurodevelopmental disorders.

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics
Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Linewidth-related bias in modelled concentration estimates from GABA-edited 1H-MRS
J-difference-edited MRS is widely used to study GABA in the human brain. Editing for low-concentration target molecules (such as GABA) typically exhibits lower signal-to-noise ratio (SNR) than conventional non-edited MRS, varying with acquisition region, volume and duration. Moreover, spectral lineshape may be influenced by age-, pathology-, or brain-region-specific effects of metabolite T2, or by task-related blood-oxygen level dependent (BOLD) changes in functional MRS contexts. Differences in both SNR and lineshape may have systematic effects on concentration estimates derived from spectral modelling. The present study characterises the impact of lineshape and SNR on GABA+ estimates from different modelling algorithms: FSL-MRS, Gannet, LCModel, Osprey, spant and Tarquin. Publicly available multi-site GABA-edited data (222 healthy subjects from 20 sites; conventional MEGA-PRESS editing; TE = 68 ms) were pre-processed with a standardised pipeline, then filtered to apply controlled levels of Lorentzian and Gaussian linebroadening and SNR reduction. Increased Lorentzian linewidth was associated with a 2-5% decrease in GABA+ estimates per Hz, observed consistently (albeit to varying degrees) across datasets and most algorithms. Weaker, often opposing effects were observed for Gaussian linebroadening. Variations are likely caused by differing baseline parametrization and lineshape constraints between models. Effects of linewidth on other metabolites (e.g., Glx and tCr) varied, suggesting that a linewidth confound may persist after scaling to an internal reference. These findings indicate a potentially significant confound for studies where linewidth may differ systematically between groups or experimental conditions, e.g. due to T2 differences between brain regions, age, or pathology, or varying T2* due to BOLD-related changes. We conclude that linewidth effects need to be rigorously considered during experimental design and data processing, for example by incorporating linewidth into statistical analysis of modelling outcomes or development of appropriate lineshape matching algorithms.

Glucose appetition in C57BL/6J mice: Influence of nonnutritive sweetener experience, food deprivation state and sex differences
In addition to its sweet taste, glucose has potent and rapid postoral actions (appetition) that enhance its reward value. This has been demonstrated by the experience-induced preference for glucose over initially preferred nonnutritive sweetener solutions in 24-h choice tests. However, some sweetener solutions (e.g., 0.8% sucralose) have inhibitory postoral actions that may exaggerate glucose appetition whereas others (e.g., 0.1% sucralose + 0.1% saccharin, S+S) do not. Experiment 1 revealed that food-restricted (FR) male C57BL/6J mice displayed similar rapid glucose appetition effects (stimulation of glucose licking within minutes) and conditioned flavor preferences following 1-h experience with flavored 0.8% sucralose or 0.1% S+S and 8% glucose solutions. Thus, the inhibitory effects of 0.8% sucralose observed in 24-h tests were not apparent in 1-h tests. Experiment 2 evaluated the effects of food deprivation state on 1-h glucose appetition. Unlike FR female mice, ad libitum (AL) fed mice displayed no or delayed stimulation of glucose licking depending upon the training solutions used (0.1% S+S vs. 8% glucose, or 0.2% S+S vs. 16% glucose). Both AL groups, like the FR group, developed a preference for the glucose-paired flavor over the S+S paired flavor. Thus, food restriction promotes glucose appetition but is not required for a conditioned preference. Overall, male and female mice showed similar glucose appetition responses although females displayed a more rapid initial glucose response.

Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
Natural scenes are highly dynamic, challenging the reliability of visual processing. Yet, humans and many animals perform accurate visual behaviors, whereas computer vision devices struggle with changing environments. How does animal vision achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control in Drosophila, resulting in stable visual processing. We identify the dendrites of specific third order neurons, Tm1 and Tm9, as the site of luminance gain control. The circuitry further involves neurons with wide-field properties, matching computational predictions that local spatial pooling can drive optimal contrast processing in natural scenes where light conditions change rapidly. Experiments and theory argue that a spatially pooled luminance signal achieves luminance gain control via divisive normalization. This process relies on shunting inhibition using the glutamate-gated chloride channel GluCl. Our work describes computationally, algorithmically, and mechanistically, how visual circuits robustly process visual information in dynamically changing, natural scenes.

Reactivated past decisions repel early sensory processing and attract late decision-making
Automatic shaping of perception by past experiences is common in many cognitive functions, reflecting the exploitation of temporal regularities in environments. A striking example is serial dependence, i.e., current perception is biased by previous trials. However, the neural implementation of its operational circle in human brains remains unclear. In two experiments with Electroencephalography (EEG) / Magnetoencephalography (MEG) recordings and delayed-response tasks, we demonstrate a two-stage 'repulsive-then-attractive' past-present interaction mechanism underlying serial dependence. First, past-trial reports serve as a prior to be reactivated during both encoding and decision-making. Crucially, past reactivation interacts with current information processing in a two-stage manner: repelling and attracting the present during encoding and decision-making, and arising in the sensory cortex and prefrontal cortex, respectively. Finally, while the early stage occurs automatically, the late stage is modulated by task and predicts bias behavior. Our findings might also illustrate general mechanisms of past-present influences in neural operations.

Characterization of the three-dimensional synaptic and mitochondrial nanoarchitecture within glutamatergic synaptic complexes in postmortem human brain via focused ion beam-scanning electron microscopy
Glutamatergic synapses are the primary site of excitatory synaptic signaling and neural communication in the cerebral cortex. Electron microscopy (EM) studies in non-human model organisms have demonstrated that glutamate synaptic activity and functioning are directly reflected in quantifiable ultrastructural features. Thus, quantitative EM analysis of glutamate synapses in ex vivo preserved human brain tissue has the potential to provide novel insight into in vivo synaptic functioning. However, factors associated with the acquisition and preservation of human brain tissue have resulted in persistent concerns regarding the potential confounding effects of antemortem and postmortem biological processes on synaptic and sub-synaptic ultrastructural features. Thus, we sought to determine how well glutamate synaptic relationships and nanoarchitecture are preserved in postmortem human dorsolateral prefrontal cortex (DLPFC), a region that substantially differs in size and architecture from model systems. Focused ion beam-scanning electron microscopy (FIB-SEM), a powerful volume EM (VEM) approach, was employed to generate high-fidelity, fine-resolution, three-dimensional (3D) micrographic datasets appropriate for quantitative analyses. Using postmortem human DLPFC with a 6-hour postmortem interval, we optimized a tissue preservation and staining workflow that generated samples of excellent ultrastructural preservation and the high-contrast staining intensity required for FIB-SEM imaging. Quantitative analysis of sub-cellular, sub-synaptic and organelle components within glutamate axo-spinous synapses revealed that ultrastructural features of synaptic function and activity were well-preserved within and across individual synapses in postmortem human brain tissue. The synaptic, sub-synaptic and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, dense reconstruction of neuropil revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of neuronal processes with heightened synaptic communication, integration and plasticity. Altogether, our findings provide a critical proof-of-concept that ex vivo VEM analysis provides a valuable and informative means to infer in vivo functioning of human brain.

A computational and multi-brain signature for aberrant social coordination in schizophrenia
Social functioning impairment is a core symptom of schizophrenia (SCZ). Yet, the computational and neural mechanisms of social coordination in SCZ under real-time and naturalistic settings are poorly understood. Here, we instructed patients with SCZ to coordinate with a healthy control (HC) in a joint finger-tapping task, during which their brain activity was measured by functional near-infrared spectroscopy simultaneously. The results showed that patients with SCZ exhibited poor rhythm control ability and unstable tapping behaviour, which weakened their interpersonal synchronization when coordinating with HCs. Moreover, the dynamical systems modelling revealed disrupted between-participant coupling when SCZ patients coordinated with HCs. Importantly, increased inter-brain synchronization was identified within SCZ-HC dyads, which positively correlated with behavioural synchronization and successfully predicted dimensions of psychopathology. Our study suggests that SCZ individuals may require stronger neural alignment to compensate for deficiency in their coordination ability. This hyperalignment may be relevant for developing inter-personalized treatment strategies.

Robust variability of grid cell properties within individual grid modules enhances encoding of local space
Although grid cells have become one of the most well studied functional classes of neurons in the mammalian brain, the assumption that there is a single grid orientation and spacing per module has remained untested. We investigate and analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties, within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the ability of encoding local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that variability, of a similar magnitude to the analyzed data, leads to significantly decreased decoding error, even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

Linking Neural Manifolds to Principles of Circuit Structure
While large-scale recording techniques indicate that the activity of heterogeneous neuronal populations lies on low-dimensional "neural manifolds", it has remained challenging to reconcile this picture with the classical view of precisely tuned neurons interacting with each other in some ordered circuit structure. Using a modelling approach, we provide a conceptual, yet mathematically precise, link between these two contrasting views. We first show that there is no unique relationship between the circuit structure and the emergent low-dimensional dynamics that characterise the population activity. We then propose a method for retrieving the circuit structure from recordings of the population activity and test it on artificial data. Our approach provides not only a unifying framework for circuit and field models on one side, and low-rank networks on the other side, but also opens the perspective to identify principles of circuit structure from large-scale recordings.

Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error
In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction errors from the previous state predictions as inputs to the hidden units of the network. We show that the time course of sequence prediction errors generated by the model closely matched the activity time courses of populations of neurons in macaque prefrontal cortex. Hidden units in the model responded to combinations of task variables and exhibited sensitivity to changing stimulus probability in ways that closely resembled monkey prefrontal neurons. Moreover, the model generated prolonged response times to infrequent, unexpected events as did monkeys. The results suggest that prefrontal cortex may generate internal models of the temporal structure of the world even during tasks that do not explicitly depend on temporal expectation, using a sequence prediction error minimization learning rule to do so. As such, the SPEL model provides a unified, general-purpose theoretical framework for modeling the lateral prefrontal cortex.

Force, angle, and velocity parameters of finger movements are reflected in corticospinal excitability
Identifying which movement parameters are reflected in the corticospinal excitability (CSE) will improve our understanding human motor control. Change in CSE measured with transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEPs) can probe the content of the signal from primary motor cortex (M1) through the corticospinal pathway and spinal motoneurons to the muscle. Here we used MEPs to investigate which movement- related parameters are reflected in CSE in 33 healthy adults. In three separate tasks, we evaluated which movement parameter(s), force, angle, and velocity, are reflected in the MEP amplitude during movement preparation and movement execution. Bayesian model comparison in a forward feature selection framework identified force and velocity measures as reflected in the MEP amplitude during movement preparation, and force, angle, and velocity measures as reflected in the MEP amplitude during movement execution. Importantly, we included measures of electromyography (EMG) in the forward feature selection, and the parameter measures are included only if they add explanatory power of MEP amplitude in addition to the EMG. These findings show that when taking EMG measures into account, all three movement parameters force, angle, and velocity are reflected in CSE. These findings propose a flexible and task-dependent form of signaling in the motor system that allows parameter-specific modulation of CSE to accurately control finger movements.

Key pointsO_LIPrior research show that the primary motor cortex activity reflects movement parameters.
C_LIO_LIMeasures of the response to a magnetic stimulation, the motor evoked potential (MEP), can be used to assess the content of the signal sent to the muscle.
C_LIO_LIWe use Bayesian model comparison to test whether movement parameters are reflected in the models best describing the MEP amplitude modulations.
C_LIO_LIWe show that the MEP amplitude reflects all tested movement parameters, force, angle, and velocity.
C_LIO_LIOur results indicate a task-dependent form of signaling not only in M1, but also in the corticospinal pathway and spinal motor neurons propagating the signal to the muscle.
C_LI

A non-Hebbian code for episodic memory
Hebbian plasticity has long dominated neurobiological models of memory formation. Yet plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. To address this, we present an episodic memory model governed by a simple non-Hebbian rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces when created via spiking and support diverse one-shot sequential and associative recall tasks, and policy learning. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning, and well-suited to encode episodes and policies as paths through a world model.

Statistical learning of incidental perceptual regularities induces sensory conditioned cortical responses
Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning of non-relevant but statistically interrelated neutral audio-visual stimuli resulted in early neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence, and in specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities can induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information of the predicted consecutive stimulus.

Drug inhibition and substrate alternating flipping mechanisms of human VMAT2
Vesicular monoamine transporters (VMAT1/2) are responsible for loading and packaging monoamine neurotransmitters into synaptic vesicles, including serotonin (5-HT), dopamine (DA), norepinephrine, and histamine. Dysregulation of VMAT2 within the central nervous system can lead to schizophrenia, mood disorders, and Parkinsons disease, due to the imbalances of these monoamine neurotransmitters. Medications such as tetrabenazine (TBZ) and valbenazine (VBZ) targetting VMAT2 are approved for treating chorea associated with Huntingtons disease and Tardive Dyskinesia. Our cryo-EM studies and molecular dynamics (MD) simulations on VMAT2 bound to drug inhibitors (TBZ and VBZ) and substrates (5-HT and DA), unveil the inhibition mechanism of VMAT2, alternating flipping mechanism of substrates during loading, translocation, and release, as well as the interplay between protonation of crucial acidic residues and substrate release. These findings enhance the understanding of VMAT-mediated monoamine neurotransmitter transport, fostering drug development for neurological and neuropsychiatric disorders, with a specific emphasis on VMATs.

Sources of uncertainty in human stereo-depth perception in natural images and scenes
2Stimulus variability--a form of nuisance variability--is a primary source of perceptual uncertainty in everyday natural tasks. How do different properties of natural scenes contribute to this uncertainty? Using binocular disparity as a model system, we report a systematic investigation of how various forms of natural stimulus variability impact performance in a stereo-depth discrimination task. In two closely related double-pass psychophysical experiments, that utilized a massive stimulus set sampled from a stereo-image database of real-world scenes, each of three human observers responded twice to ten thousand unique trials containing twenty thousand unique stimuli. New analytical methods reveal, from this data, the specific and nearly dissociable effects of two distinct sources of natural stimulus variability--luminance-contrast variation and local-depth variation--on discrimination performance, as well as the relative importance of stimulus-driven-variability and internal-noise in determining performance limits. Further, between-observer analyses show that both stimulus-driven sources of uncertainty make stimulus-by-stimulus responses more predictable (not less), are responsible for a large proportion of total variance, and have strikingly similar effects on different people. The consistency across observers raises the intriguing prospect that image-computable models can make reasonably accurate performance predictions in natural viewing. Overall, the findings provide a rich picture of stimulus factors that contribute to human perceptual performance in natural scenes. The approach should have broad application to other animal models and other sensory-perceptual tasks with natural or naturalistic stimuli.

1 SignificanceLinking properties of the external world, and of sensory stimuli, to how neurons and animals respond has proven an important approach to understanding how the brain works. Much is known about how nervous systems respond to simple stimuli. Less is known about how systems respond during real-world viewing. Using data from two human stereo-depth discrimination experiments, we develop a new approach that reveals how distinct features of natural scenes and images contribute to perceptual performance. Results show that stimulus-by-stimulus variation has highly consistent effects on different people. The approach should have broad application to other animal models and other sensory-perceptual tasks.

Interneuron loss and microglia activation by transcriptome analyses in the basal ganglia of Tourette syndrome
Tourette syndrome (TS) is a disorder of high-order integration of sensory, motor, and cognitive functions afflicting as many as 1 in 150 children and characterized by motor hyperactivity and tics. Despite high familial recurrence rates, a few risk genes and no biomarkers have emerged as causative or predisposing factors. The syndrome is believed to originate in basal ganglia, where patterns of motor programs are encoded. Postmortem immunocytochemical analyses of brains with severe TS revealed decreases in cholinergic, fast-spiking parvalbumin, and somatostatin interneurons within the striatum (caudate and putamen nuclei). Here, we performed single cell transcriptomic and chromatin accessibility analyses of the caudate nucleus from 6 adult TS and 6 control post-mortem brains. The data reproduced the known cellular composition of the adult human striatum, including a majority of medium spiny neurons (MSN) and small populations of GABAergic and cholinergic interneurons. Comparative analysis revealed that interneurons were decreased by roughly 50% in TS brains, while no difference was observed for other cell types. Differential gene expression analysis suggested that mitochondrial function, and specifically oxidative metabolism, in MSN and synaptic function in interneurons are both impaired in TS subjects, while microglia display strong activation of immune response pathways. Our data explicitly link gene expression changes to changes in cis-regulatory activity in the corresponding cell types, suggesting de-regulation as a factor for the etiology of TS. These findings expand on previous research and suggest that impaired modulation of striatal function by interneurons may be the origin of TS symptoms.

Distinct Dendritic Morphological Changes in the Nucleus Accumbens of Microbiota-deficient Male Mice
The gut microbiota has been shown to be an important regulator of brain and behaviour. Germ-free rodents are a key model to study the microbiome-gut-brain axis to reveal the microbial underpinnings of diseases, including those related to psychiatric illnesses. The present study evaluated whether the absence of gut microbiota could alter the morphological development of the nucleus accumbens, a brain region located in the ventral striatum involved in stress, mood and addiction. In germ-free mice, there was dendritic hypertrophy of medium spiny neurons in the shell and dendritic elongation in the core. This led to an increase in the number of stubby dendritic spines within the shell and an increase in both stubby and thin spines in the core. Taken together, these results indicate that the gut microbiota is essential for the normal development of the dendritic structure of medium spiny neurons in the nucleus accumbens and that altered remodelling may contribute to maladaptive psychiatric disorders.

Linking neuronal avalanches with oscillatory and broadband 1/f activities in the resting human brain
ObjectiveBrain oscillations, broadband 1/f activity and neuronal avalanches (NA) are valuable conceptualizations extensively used to interpret brain data, yet, these perspectives have mainly progressed in parallel with no current consensus on a rationale linking them. This study aims to reconcile these viewpoints using source-reconstructed MEG data obtained in healthy humans during eyes-closed resting state.

MethodsWe analyzed NA in source-reconstructed MEG data from 47 subjects. For this, we introduced custom measures and a comprehensive array of features characterizing the statistical, spatiotemporal and spectral properties of NA. By using the complex baseband representation of signals we provide an analytical description of the mechanisms underlying the emergence of NA from the Fourier spectral constituents of the brain activity.

ResultsThe observed NA disclose a significant spectral signature in the alpha band, suggesting that the large-scale spreading of alpha bursts occurs mainly via brain avalanches. Besides, the NA detected in our MEG dataset can be segregated based on their spectral signature in two main groups having different propagation patterns, where cluster 2 avalanches is mainly related to the spread of narrowband alpha bursts across the brain network, whereas cluster 1 avalanches correspond to more spatially localized fluctuations promoted by the broadband 1/f activity. We also provide an analytical framework for the evidence showing that a) spectral group delay consistency in specific narrow frequency bands, b) transient cross-regional coherent oscillations and c) broadband 1/f activity, are all key ingredients for the emergence of realistic avalanches.

SignificanceThe proposed analytical arguments, supported by extensive model and experimental evidence, show how NA emerge from narrowband oscillations and broad-band arrhythmic activity co-existing in the human brain. Our results suggest that large-scale spreading of specific narrowband oscillations takes place in a transient manner mainly via NA, which may play a functional role as a long-range interaction mechanism in the resting human brain.

HighlightsO_LINeuronal avalanches propagating across the brain during spontaneous resting state activity, are highly structured in terms of their spatial, temporal and spectral properties.
C_LIO_LIThe link between local above-threshold fluctuations and oscillations can be understood in terms of the group delay consistency across the spectral components of the neuronal activity (spectral group delay consistency).
C_LIO_LISpectral group delay consistency, transient cross-regional coherent oscillations and broadband 1/f activity, are all key ingredients for the emergence of realistic avalanches.
C_LIO_LIObserved neuronal avalanches can be segregated based on their spectral signature in two main groups having different propagation patterns, where cluster 2 avalanches is specifically related to the spread of narrowband alpha bursts across the brain network, whereas cluster 1 avalanches correspond to more spatially localized fluctuations promoted by the broadband 1/f activity.
C_LIO_LILarge-scale spreading of alpha bursts occurs mainly via brain avalanches, which may play a functional role as a long-range interaction mechanism in the resting human brain.
C_LI

Synchronized photoactivation of T4K rhodopsin causes a chromophore-dependent retinal degeneration that moderated by interaction with phototransduction cascade components.
Multiple mutations in the Rhodopsin gene cause sector retinitis pigmentosa in humans and a corresponding light-exacerbated retinal degeneration (RD) in animal models. Previously we have shown that the rhodopsin mutation T4K requires photoactivation to exert its toxic effect. Here we further investigated the mechanisms involved in rod cell death caused by T4K rhodopsin in Xenopus laevis. In this model, RD was prevented by rearing animals in constant darkness but surprisingly also in constant light. RD was maximized by light cycles containing at least one hour of darkness and 20 minutes of light exposure, light of intensity 750 lux or greater, and by sudden light onset. Under conditions of frequent light cycling, RD occured rapidly and synchronously, with massive shedding of ROS fragments into the RPE initiated within hours, and subsequent death and phagocytosis of rod cell bodies. RD was minimized by reduced light levels, pre-treatment with constant light, and gradual light onset. RD was prevented by genetic ablation of the retinal isomerohydrolase RPE65, and exacerbated by ablation of phototransduction components GNAT1, SAG, and GRK1. Our results indicate that photoactivated T4K rhodopsin is toxic, that cell death requires synchronized photoactivation of T4K rhodopsin, and that toxicity is mitigated by interaction with other rod outer segment proteins regardless of whether they participate in activation or shutoff of phototransduction. In contrast, RD caused by P23H rhodopsin does not require photoactivation of the mutant protein, as it was exacerbated by RPE65 ablation, suggesting that these phenotypically similar disorders may benefit from different treatment strategies.

SignificanceA large number of rhodopsin mutations are linked to the inherited degenerative disease retinitis pigmentosa. Although the end result in each case is the loss of photoreceptor cells and blindness, not all of these mutations cause cell death via the same mechanism. In order to design and test treatment therapies that target the disease at points as upstream as possible in the process, we require detailed understanding of the range and nature of these disease mechanisms. This study using a transgenic Xenopus laevis model has extended our understanding of how T4K rhodopsin and related mutations cause rod cell photoreceptor death via a phototoxic product, and how this mechanism differs from the more extensively researched protein misfolding mechanism underlying cell death caused by P23H rhodopsin.

Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators.
Neuromodulatory processes in the brain can critically change signal processing on a cellular level leading to dramatic changes in network level reorganization. Here, we use coupled non-identical Kuramoto oscillators to investigate how changes in the shape of phase response curves from Type 1 to Type 2, mediated by varying ACh levels, coupled with activity dependent plasticity may alter network reorganization. We first show that when plasticity is absent, the Type 1 networks, as expected, exhibit asynchronous dynamics with oscillators of the highest natural frequency robustly evolving faster in terms of their phase dynamics. At the same time, the Type 2 networks synchronize, with oscillators locked so that the ones with higher natural frequency have a constant phase lead as compared to the ones with lower natural frequency. This relationship establishes a robust mapping between the frequency and oscillators phases in the network, leading to structure/frequency mapping when plasticity is present. Further we show that while connection plasticity can produce stable synchrony (so called splay states) in Type 1 networks, the structure/frequency reorganization observed in Type 2 networks is not present.

Reconstructing Voice Identity from Noninvasive Auditory Cortex Recordings
The cerebral processing of voice information is known to engage Temporal Voice Areas (TVAs) that respond preferentially to conspecific vocalizations. But how voice information related to the stable physical characteristics of the speaker such as gender, age or identity is represented by neuronal populations in these areas remains poorly understood. Here we used a deep neural network (DNN) to generate a high-level, small-dimension representational space of voice stimuli--the voice latent space (VLS)--and examined its linear relation with cerebral activity via encoding, representational similarity and decoding analyses. We find that the VLS maps onto fMRI measures of cerebral activity in response to tens of thousands of voice stimuli from hundreds of different speaker identities, and better accounts for the representational geometry for speaker identity in the TVAs than in A1. Moreover, the VLS allowed TVA-based reconstructions of voice stimuli that preserved important aspects of speaker gender and identity as assessed by both machine classifiers and human listeners. These results demonstrate that a low-dimensional, DNN-derived space accounts well for cerebral voice representations and provide insights into representational differences between A1 and the TVAs, paving the way to noninvasive brain-computer interface applications.

The Douglas Bell Canada Brain Bank Post-mortem Brain Imaging Protocol
Magnetic resonance imaging (MRI) is a valuable non-invasive tool that has been widely used for in vivo investigations of brain morphometry and microstructural characteristics. Postmortem MRIs can provide complementary anatomical and microstructural information to in vivo imaging and ex vivo neuropathological assessments without compromising the sample for future investigations. We have developed a postmortem MRI protocol for the brain specimens of the Douglas-Bell Canada Brain Bank (DBCBB), the largest brain bank in Canada housing over 3000 neurotypical and diseased brain specimens, that allows for acquisition of high-resolution 3T and 7T MRIs. Our protocol can be used to scan DBCBB specimens with minimal tissue manipulation, allowing for feasibly scanning large numbers of postmortem specimens while retaining the quality of the tissue for downstream histology and immunohistochemistry assessments. We demonstrate the robustness of this protocol in spite of the dependency of image quality on fixation by acquiring data on the first day of extraction and fixation, to over twenty years post fixation. The acquired images can be used to perform volumetric segmentations, cortical thickness measurements, and quantitative analyses which can be potentially used to link MRI-derived and ex vivo histological measures, assaying both the normative organization of the brain and ex vivo measures of pathology.

A nonlinear code for event probability in the human brain
Assessing probabilities and predicting future events are fundamental for perception and adaptive behavior, yet the neural representations of probability remain elusive. While previous studies have shown that neural activity in several brain regions correlates with probability-related factors such as surprise and uncertainty, similar correlations have not been found for probability. Here, using 7 Tesla functional magnetic resonance imaging, we uncover a representation of the probability of the next event in a sequence within the human dorsolateral prefrontal and intraparietal cortices. Crucially, univariate and multivariate analyses revealed that this representation employs a highly nonlinear code. Tuning curves for probability exhibit selectivity to various probability ranges, while the code for confidence accompanying these estimates is predominantly linear. The diversity of tuning curves we found recommends that future studies move from assuming linear correlates or simple canonical forms of tuning curves to considering richer representations whose benefits remain to be discovered.

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks
Conventional modeling of the Blood-Oxygen-Level-Dependent (BOLD) signal in resting-state functional Magnetic Resonance Imaging (rsfMRI) struggle with parameter estimation due to the complexity of brain dynamics. This study introduces a novel brain dynamics model (BDM) that directly captures BOLD signal variations through differential equations. Unlike dynamic causal models or neural mass models, we integrate hemodynamic responses into the signal dynamics, considering both direct and network-mediated neuronal activity effects. We utilize Physics-Informed Neural Networks (PINNs) to estimate the parameters of this BDM, leveraging their ability to embed physical laws into the learning process. This approach simplifies computational demands and increases robustness against data noise, providing a comprehensive tool for analyzing rsfMRI data. Leveraging the functional connectivity matrices scaled by the estimated parameters, we apply a state-of-the-art community detection method to elucidate the network structure. Our analysis reveals significant differences in the participation coefficients of specific brain regions when comparing neurotypical individuals to those with Autism Spectrum Disorder (ASD), with distinct patterns observed between male and female cohorts. These differences are consistent with regions implicated in previous studies, reinforcing the role of these areas in ASD. By integrating PINNs with advanced network analysis, we demonstrate a robust approach for dissecting the complex neural signatures of ASD, providing a promising direction for future research in neuroimaging and the broader field of computational neuroscience.

A Massively Parallel CRISPR-Based Screening Platform for Modifiers of Neuronal Activity
Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders. In this study, we develop pooled screens for neuronal activity, using CRISPR interference (CRISPRi) and the fluorescent calcium integrator CaMPARI2. Using this screening method, we identified 153 genes associated that changed synaptic excitability in human iPSC-derived neurons, revealing potential links to neurodegenerative and neurodevelopmental disorders. These genes include known regulators of neuronal excitability, such as TARPs and ion channels, as well as genes associated with autism spectrum disorder (ASD) and Alzheimers disease (AD) not previously described to affect neuronal excitability. This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal activity in health and disease.

A ventral hippocampal-lateral septum pathway regulates social novelty preference.
The ability to distinguish strangers from familiar individuals is crucial for the survival of most mammalian species. In humans, an inability to recognize kin and familiar individuals and engage in appropriate behaviors is associated with several types of dementia, including Alzheimer's disease. Mice preferentially spend more time investigating a novel individual relative to a familiar individual. Yet, how social novelty related information drives increased investigation of the novel animal remains poorly understood. Recent evidence has implicated the ventral hippocampus (vHPC) as a key node in encoding information about conspecific identity. Of particular interest are vHPC projections to the lateral septum (LS), a region that has been implicated in driving a wide range of motivated social behaviors. In this study using chemogenetics, optogenetics and monosynaptic rabies tracing, we identified a novel vHPC-LS-ventral tegmental area (VTA) pathway that is necessary for mice to preferentially investigate novel conspecifics. Using monosynaptic rabies tracing, we established that LS neurons make direct monosynaptic connections onto dopaminergic neurons in the VTA. Thus, we have identified a potential pathway via which conspecific identity could be transformed to drive motivated social behaviors.

Metaplastic neuromodulation via transcranial direct current stimulation has no effect on corticospinal excitability and neuromuscular fatigue
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation tool with potential for managing fatigue, possibly due to alterations in corticospinal excitability. However, inconsistencies in intra- and inter- individual variability responsiveness to tDCS limit its clinical use. Emerging evidence suggests harnessing homeostatic metaplasticity induced via tDCS may reduce variability and boost its outcomes, yet little is known regarding its influence on fatigue in healthy adults. We explored whether cathodal tDCS (ctDCS) prior to exercise combined with anodal tDCS (atDCS) could augment corticospinal excitability and attenuate fatigue. 15 young healthy adults (6 males, 22 +/- 4 years) participated in four pseudo-randomised neuromodulation sessions: sham stimulation prior and during exercise, sham stimulation prior and atDCS during exercise, ctDCS prior and atDCS during exercise, ctDCS prior and sham stimulation during exercise. The exercise constituted an intermittent maximal voluntary contraction (MVC) of the right first dorsal interosseous (FDI) for 10 minutes. Fatigue was quantified as an attenuation in MVC force, while motor evoked potential (MEP) amplitude provided an assessment of corticospinal excitability. MEP amplitude increased during the fatiguing exercise, whilst across time, force decreased. There were no differences in MEP amplitudes or force between neuromodulation sessions. These outcomes highlight the ambiguity of harnessing metaplasticity to ameliorate fatigue in young healthy individuals.

Electrophysiological approaches to understanding brain-muscle interactions during gait: a systematic review
ObjectiveThis study systematically reviews the role of the cortex in gait control by analyzing connectivity between electroencephalography (EEG) and electromyography (EMG) signals, i.e. neuromuscular connectivity (NMC) during walking. We aim to answer the following questions: (i) Is there significant NMC during gait in a healthy population? (ii) Is NMC modulated by gait task specifications (e.g. speed, surface, additional task demands)? (iii) Is NMC altered in the elderly or a population affected by a neuromuscular or neurologic disorder?

MethodsFollowing PRISMA guidelines, a systematic search of seven scientific databases was conducted until September 2023.

ResultsOut of 1308 identified papers, 27 studies met the eligibility criteria. Despite large variability in methodology, significant NMC was detected in most of the studies. NMC was able to discriminate between a healthy population and a population affected by a neuromuscular or neurologic disorder. Tasks requiring higher sensorimotor control resulted in an elevated level of NMC.

ConclusionsWhile NMC holds promise as a metric for advancing our comprehension of brain-muscle interactions during gait, aligning methodologies across studies is imperative.

SignificanceAnalysis of NMC provides valuable insights for the understanding of neural control of movement, development of gait retraining programs and contributes to advancements in neurotechnology.

A mediating framework in resting-state connectivity between the medial prefrontal cortex and anterior cingulate in mild cognitive impairment
Mild cognitive impairment (MCI) is recognized as the prodromal phase of dementia, a condition that can be either maintained or reversed through timely medical interventions to prevent cognitive decline. Considerable studies using functional magnetic resonance imaging (fMRI) have indicated that altered activity in the medial prefrontal cortex (mPFC) serves as an indicator of various cognitive stages of aging. However, the impacts of intrinsic functional connectivity in the mPFC as a mediator on cognitive performance in individuals with and without MCI have not been fully understood. In this study, we recruited 42 MCI patients and 57 healthy controls, assessing their cognitive abilities and functional brain connectivity patterns through neuropsychological evaluations and resting-state fMRI, respectively. The MCI patients exhibited poorer performance on multiple neuropsychological tests compared to the healthy controls. At the neural level, functional connectivity between the mPFC and the anterior cingulate cortex (ACC) was significantly weaker in the MCI group and correlated with multiple neuropsychological test scores. The result of the mediation analysis further demonstrated that functional connectivity between the mPFC and ACC notably mediated the relationship between the MCI and semantic working memory. These findings suggest that altered mPFC-ACC connectivity may have a plausible causal influence on cognitive decline and provide implications for early identifications of neurodegenerative diseases and precise monitoring of disease progression.

On Monitoring Brain Health from the Depths of Sleep: Feature Engineering and Machine Learning Insights for Digital Biomarker Development
BackgrounSingle-channel sleep electroencephalography (EEG) is a promising technology for creating cost-effective and widely accessible digital biomarkers for monitoring brain health. Sleep, notable for its numerous connections to brain health, is of particular interest in this context. Indeed, several of the best studied and widely recognized risk factors for neurodegenerative disease are also connected to aspects of sleep physiology, including biological sex, hypertension, diabetes, obesity/metabolic dysregulation, and immune system dysfunction. In this study, we utilize the unique signal characteristics of slow wave sleep (SWS) oscillatory events as features in machine learning models to predict underlying biological processes that are highly relevant to brain health. Our objective is to establish a foundation for algorithms capable of effectively monitoring physiological processes in sleep that directly and indirectly inform brain health using single-channel sleep EEG as a functional metric of brain activity.

MethodsUtilizing data from the Cleveland Family Study, we analyzed 726 overnight polysomnography recordings to extract features from slow waves and adjacent oscillatory events. Advanced signal processing and machine learning techniques, including random forest models, were employed to engineer features and predict health-related outcomes such as age, cerebrovascular risk factors, endocrine functions, immune system activity, and sleep apnea.

ResultsOur models demonstrated significant predictive capability for several outcomes, including age (R2 = 0.643, p < 0.001), and sex classification (area under the receiver operator characteristic (AUROC) curve = 0.808), diabetes and hypertension diagnosis (AUROC = 0.832 and 0.755, respectively). Significant predictions were also modeled for metabolic/endocrine functions (including blood concentrations of IGF-1, leptin, ghrelin, adiponectin, and glucose), and immune markers (including IL-6, TNF-alpha, and CRP). In addition, this approach provided successful predictions in regression modeling of BMI and both regression and classification of sleep apnea.

DiscussionThis study demonstrates the potential of using features from oscillatory events in single-channel sleep EEG as digital biomarkers. These biomarkers can identify key health and demographic factors that both affect brain health and are indicative of core brain functions. By capturing the complex interactions of neural, metabolic, endocrine, and immune systems during sleep, our findings support the development of single-channel EEG as a practical tool for monitoring complex biological processes through metrics that originate in brain physiology. Future research should aim to refine these digital biomarkers for broader home-based applications that may utilize inexpensive "wearable" devices to provide a scalable and accessible tool for tracking brain health-related outcomes.

Blood metabolomic profiling reveals new targets in the management of psychological symptoms associated with alcohol use disorder
Alcohol use disorder (AUD) is a global health problem with limited therapeutic options. The biochemical mechanisms that lead to alcohol addiction are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD. The plasma metabolome contains a plethora of bioactive molecules that reflects the functional changes in host metabolism but also the impact of the gut microbiome and nutritional habits. In this study, we investigated the impact of chronic alcohol abuse, and of a three-week period of alcohol abstinence, on the blood metabolome (non-targeted LC-MS metabolomics analysis) in 96 patients diagnosed with severe alcohol use disorder (AUD). We found that the plasma levels of different lipids ((lyso)phosphatidylcholines, long-chain fatty acids), short-chain fatty acids (i.e. 3-hydroxyvaleric acid) and bile acids were altered in AUD patients. In addition, several microbial metabolites, including indole-3-propionic acid, p-cresol sulfate, hippuric acid, pyrocatechol sulfate, and metabolites belonging to xanthine class (paraxanthine, theobromine and theophylline) were sensitive to alcohol abuse and alcohol withdrawal. 3-Hydroxyvaleric acid, caffeine metabolites (theobromine, paraxanthine and theophylline) and microbial metabolites (hippuric acid and pyrocatechol sulfate) were correlated with anxiety, depression and alcohol craving. Metabolomics analysis in post-mortem samples of frontal cortex and cerebrospinal fluid of those consuming a high level of alcohol revealed that those metabolites can be found also in brain tissue. Our data allow to for the identification of neuroactive metabolites, from interactions between food components and microbiota, which may represent new targets in the management of neuropsychiatric diseases such as AUD.

The study was registered at clinicaltrial.gov under the identification number NCT03803709.

Syngap1 regulates the synaptic drive and membrane excitability of Parvalbumin-positive interneurons in mouse auditory cortex
SYNGAP1 haploinsufficiency-related intellectual disability (SYNGAP1-ID) is characterized by moderate to severe ID, generalized epilepsy, autism spectrum disorder, sensory processing dysfunction and other behavioral abnormalities. While most studies, so far, have focussed on the role of Syngap1 in cortical excitatory neurons, recent studies suggest that Syngap1 plays a role in GABAergic inhibitory neuron development as well. However, the molecular pathways by which Syngap1 acts on GABAergic neurons, and whether they are similar or different from the mechanisms underlying its effects in excitatory neurons, is unknown. Here we examined whether, and how, embryonic-onset Syngap1 haploinsufficiency restricted to GABAergic interneurons derived from the medial ganglionic eminence (MGE) impacts their synaptic and intrinsic properties in adulthood. We found that Syngap1 haploinsufficiency affects the intrinsic properties, overall leading to increased firing threshold, and decreased excitatory synaptic drive of Parvalbumin (PV)+ neurons from Layer IV auditory cortex in adult mice, whilst Somatostatin (SST)+ interneurons were mostly resistant to Syngap1 haploinsufficiency. Further, the AMPA component of thalamocortical evoked-EPSC was decreased in PV+ cells from mutant mice. Finally, we found that targeting the Kv1 family of voltage-gated potassium channels was sufficient to rescue PV+ mutant cell-intrinsic properties to wild-type levels. Together, these data suggest that Syngap1 plays a specific role in the maturation of PV+ cell intrinsic properties and synaptic drive, and its haploinsufficiency may lead to reduced PV cell recruitment in the adult auditory cortex, which could thus underlie the auditory processing alterations found in SYNGAP1-ID preclinical models and patients.